{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amended-composer",
   "metadata": {},
   "source": [
    "框架\n",
    "\n",
    "    一、导入数据\n",
    "    二、原始序列的检验\n",
    "    三、一阶差分序列的检验\n",
    "    四、定阶（参数调优)\n",
    "    五、建模与预测\n",
    "    \n",
    "    参考材料：《Python数据分析与挖掘实践》"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-measurement",
   "metadata": {},
   "source": [
    "## 工具准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "short-partner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from matplotlib.pylab import style #自定义图表风格\n",
    "style.use('ggplot')\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#pip install statsmodels\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf  #自相关图、偏自相关图\n",
    "from statsmodels.tsa.stattools import adfuller as ADF #平稳性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox #白噪声检验\n",
    "import statsmodels.api as sm #D-W检验,一阶自相关检验\n",
    "from statsmodels.graphics.api import qqplot #画QQ图,检验一组数据是否服从正态分布\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-metabolism",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "israeli-ballot",
   "metadata": {},
   "outputs": [],
   "source": [
    "yearAndMonth = [\n",
    "    \"2019/10\",\n",
    "    \"2019/11\",\n",
    "    \"2019/12\",\n",
    "    \"2020/1\",\n",
    "    \"2020/2\",\n",
    "    \"2020/3\",\n",
    "    \"2020/4\",\n",
    "    \"2020/5\",\n",
    "    \"2020/6\",\n",
    "    \"2020/7\",\n",
    "    \"2020/8\",\n",
    "    \"2020/9\",\n",
    "    \"2020/10\",\n",
    "    \"2020/11\",\n",
    "    \"2020/12\",\n",
    "    \"2021/1\",\n",
    "    \"2021/2\",\n",
    "    \"2021/3\",\n",
    "    \"2021/4\",\n",
    "    \"2021/5\",\n",
    "    \"2021/6\",\n",
    "    \"2021/7\",\n",
    "    \"2021/8\",\n",
    "    \"2021/9\"\n",
    "]\n",
    "\n",
    "salesData = [\n",
    "    \"13.51\",\n",
    "    \"14.75\",\n",
    "    \"13.12\",\n",
    "    \"12.81\",\n",
    "    \"0.71\",\n",
    "    \"4.15\",\n",
    "    \"14.36\",\n",
    "    \"13.15\",\n",
    "    \"15.74\",\n",
    "    \"15.09\",\n",
    "    \"14.58\",\n",
    "    \"18.14\",\n",
    "    \"17.19\",\n",
    "    \"17.98\",\n",
    "    \"19.22\",\n",
    "    \"14.80\",\n",
    "    \"8.71\",\n",
    "    \"14.55\",\n",
    "    \"15.12\",\n",
    "    \"11.28\",\n",
    "    \"10.65\",\n",
    "    \"10.14\",\n",
    "    \"8.41\",\n",
    "    \"13.62\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sonic-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 24 entries, 2019/10 to 2021/9\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   sale    24 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 384.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019/10</th>\n",
       "      <td>13.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019/11</th>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019/12</th>\n",
       "      <td>13.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/1</th>\n",
       "      <td>12.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/2</th>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/3</th>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/4</th>\n",
       "      <td>14.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/5</th>\n",
       "      <td>13.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/6</th>\n",
       "      <td>15.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/7</th>\n",
       "      <td>15.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/8</th>\n",
       "      <td>14.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/9</th>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/10</th>\n",
       "      <td>17.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/11</th>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020/12</th>\n",
       "      <td>19.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/1</th>\n",
       "      <td>14.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/2</th>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/3</th>\n",
       "      <td>14.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/4</th>\n",
       "      <td>15.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/5</th>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/6</th>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/7</th>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/8</th>\n",
       "      <td>8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021/9</th>\n",
       "      <td>13.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sale\n",
       "date          \n",
       "2019/10  13.51\n",
       "2019/11  14.75\n",
       "2019/12  13.12\n",
       "2020/1   12.81\n",
       "2020/2    0.71\n",
       "2020/3    4.15\n",
       "2020/4   14.36\n",
       "2020/5   13.15\n",
       "2020/6   15.74\n",
       "2020/7   15.09\n",
       "2020/8   14.58\n",
       "2020/9   18.14\n",
       "2020/10  17.19\n",
       "2020/11  17.98\n",
       "2020/12  19.22\n",
       "2021/1   14.80\n",
       "2021/2    8.71\n",
       "2021/3   14.55\n",
       "2021/4   15.12\n",
       "2021/5   11.28\n",
       "2021/6   10.65\n",
       "2021/7   10.14\n",
       "2021/8    8.41\n",
       "2021/9   13.62"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转换数据类型\n",
    "index = pd.DatetimeIndex(yearAndMonth)\n",
    "data = {\"date\":yearAndMonth,\"sale\":salesData}\n",
    "\n",
    "sale = pd.DataFrame(data)\n",
    "# 转换数据类型\n",
    "sale.set_index(['date'],inplace=True)\n",
    "sale.sale=sale.sale.astype('float')\n",
    "\n",
    "sale.info()\n",
    "sale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-assistant",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "unsigned-photography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-2.4712055302724574, 0.12263842780065559, 9, 14, {'1%': -4.01203360058309, '5%': -3.1041838775510207, '10%': -2.6909873469387753}, 70.14930065960837)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  6.606452   0.010161\n"
     ]
    }
   ],
   "source": [
    "sale.plot()\n",
    "plt.show()\n",
    "print('原始序列的ADF检验结果为：',ADF(sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "accomplished-aggregate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-4.561375860433326, 0.0001520838938266275, 1, 21, {'1%': -3.7883858816542486, '5%': -3.013097747543462, '10%': -2.6463967573696143}, 70.6571063340017)\n",
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  0.281618   0.595643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1_sale=sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d1_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "plot_acf(d1_sale,lags=22).show()\n",
    "plot_pacf(d1_sale,lags=10).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d1_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d1_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "single-carroll",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-6.182485543274925, 6.405219754413682e-08, 1, 20, {'1%': -3.8092091249999998, '5%': -3.0216450000000004, '10%': -2.6507125}, 70.47248707929624)\n",
      "一阶差分序列的白噪声检验结果为：    lb_stat  lb_pvalue\n",
      "1   3.2818   0.070052\n"
     ]
    }
   ],
   "source": [
    "d2_sale=d1_sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d2_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "# plot_acf(d2_sale,lags=9).show()\n",
    "# plot_pacf(d2_sale,lags=4).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d2_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d2_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "affected-majority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HzZtK6Wgxp0IqWq5Jjr4JHe14Ntw59jGlZgGmpdZ80pJWmmXLli0AlJWV8dhjj1FXV8fHP/5xPJ5JWr7eERhsoEiGiJjr9jYcz5h3tJtxcUPLEqPMuA68XqloucbQe18zls4r1ox9UIkPlEdW5pMYy1TX5/NRU1MzanN0UpGsL0uUSEu/G1bm0e7PuGmWnByYWSkr82sI3d2Ffms/6uZbUd7cMY9TOTlG0KXWfNIySZfQ1qO1jviypCDmBdPM0GQ35MzbBicMxaWiSlbm1xD68B642o8aL8USxecXf5ZJjIh5lO4uGBhILWeuFJ7SGa5Ymet4vixDUOVV0NyEvno1g1EJTqH37YCZlTB/8YTHypAKe9FaM/DtJ+h59SVbri9iHiXSys/0kpROzymd4Y7Bzm2RcXFjvSmVV5mc+uWLGQ1LyDy65TK89w5qwx2J9T/4/NDWYj6lCtZztgHePmDbLF4R8yiRQc4p5cwBT6nfHYOd21ugqHTMsXeqYrb5QvLmWY/etwMAte6OxE4o9cPVfugM2haTVehAC/rcKafDSAq9Zzt4veTdsmnig1NAxDxKzDExRTEv8Q2u7h1Et41RlhjlOtN0IJug2Y3W2oj5omWosvKEzpksteZaa8Lf+VfC/+Mr6NDkSBfqUAh9YCfc+D4804tseQ4R8wgxX5YU0ywe3wzo6nA+Fx1oGayuiYPKyzcfpy+ezWBQQsY5fQKazg33LZ8IX6TZz+215kcOwqnj0N0JdUecjiYxag9PXOufJiLmUToCoBQUpvaumROtTw86nDcPtKDG2PyMUT4b3XQ+M/EIjqD3vQbeXNTNtyR+0iSYOKTDYcK/ftZ4suflo9/Y43RICRHeu91oy3i1/mkiYh4lGIBp0029bQp4oqkNB/Pmuq/PVOWMVZYYQVVUQdM52ejKUqIf6dVN70MVJOHBU1hsmspcXGuu33gdzp1GbX4IdeP70G/tQw8MOB3WuOiuDnj7AGrd7SivJXZYcRExj6A72lPzZYkQE3MnK1oCE9SYRymfBX29gzXpQnZRexg6g6gNG5M6TXk8rvY11wMD6P/4GVTOMTNM19RAZwccf9fp0MZFH9wNoVDS/x/JImIeJUVfliieSJrF0fLEaPfneBugDK1okbz5ULTW6Lf2o/v7nA4lLfTe18zCZPmq5E8u9aNdWmuu9+2ApvN4Nj9kqrWWr4YpU81q3cXofa9B5RyYU23r84iYR0m1lT+Cp6jEeFs42AWqA3EmDMUjOkLuouTNh3H6BOGn/htdLzzjdCQpo7s70W8fQK29NaWP9MrnzpW5Dl1F/+e/wdyFsGo9YObvqhtuRh/eiw67M9Wim87DyTpUzUbbZx2ImEfpCKS1Mlc5Oaas0cnyxHizP+NRVAL506TWfAS63nxc7375BXTv5Bx9qA+9DqGribXvx6PUD4EW14mj3vUHaLmM508fGi6Ka24xZcUnjjkX3Djofa+B8qDW3W77c4mYY9716e5KK2cOQHHp4OrYCdpaYGr+hAMylFJQPgst5YnD0PXHYGo+urMDvfv3ToeTEnrfa+aT19yFqV3A5zcdwu0BS+NKB93fh/7N87BwmUmtDEHdsAZyp7iyqkWHwybldf1NcY3vrEbEHEy+HFJuGIpR7HN0A1QHWsb0ZBmJqpgNUp4YQ2sNJ46i1tSQu+wm9B/+HR0KOR1WUugrTVB/FLU+wfb9OKhorbmLKlr0jt9Ceyuej3xq1OtSefmwfDX68B7j5e8m6muh9Qqqxt6Nzygi5hDr/lQpNgxFUcWlDleztE6cL49SXgXtrejuLntjmiw0nTdt7IuWMe0jn4LWZtOxN4nQ+3cAJNcoNJJoF6hLNkF1bzf65V/CspWoxSviHqPW1Jjf/Yb3Mhzd+Og92yEvH7VyfUaeT8QcYr4sVqRZCAacyze2tST8cU5VzDJfSN4cAF1fC4BauIwpa2pg1lz0715032pvDLTW5iP9khtQM9IY2xidOOSSTVC97T+hM4jnTz815jHqxrXg9boq1aL7etFv7EGtuQU1dWpGnlPEnCGt/FakWXQYOjJvVGTGxbUlnGah3JQnikdLhBNHzZv5dZUopVB33w8XGuGdN5yOLDEa3oPLF1Hr70jvOgXTYGqeK9IsuqsT/ftfw8p1qHEsfFXBNFi2yqRaXNIIp9/cB309tteWD0XEHIaYbJWkdRlVHDnfifLEziAMhBJPs/ivgxyvrMwj6PqjxpQqkpNVa28FXxnhV37pcGSJofftgNwpqDVJtO/HQSnlmlpz/bsXobcbz+aHJjxWrakxb0CnT2QgsonRe7fDjJmwaFnGnnNSibm+eJbOn/3AtK1bSUfATAqamp/edWLj4wLpRpQ8sXFxCW6Aer0ws0JqzQHd1gLNl1BD/vCU14v6k81w4ijapWVvUXToKvrgLtTKdRNWMiWEC2rNdbAN/ep/om5+P6pq3oTHq5vWQU6OKxqIdFsLHHsbteFO01WbISaVmHPpAl3/5yfG5N1KggGYXpx+UX9ssLMDK/O2BBuGhlJR5aouUK01ur0NXXcEfTZzXtX6xFHA5MuHom69C6ZNd//q/J03oKsj9dryEbhh4pB++ZemXv7DDyZ0vJpWCEtvdEWqRe/bAVpb9v+RKPa5vtjBPFM7q8+cQC283rLLpuvLEiMi5k5UtOhEfVmGoMqr0G8fQIdCthoAjUSHQtDcRO/Jo4SPH4WL50zuvuk89ESqa/IL8Hz9p+MOIbaM+qMmTzx7eLu1mpqHuvOD6Jd+jr7QiKqcY38sKRDev8P8/i5LoX0/Hj6/2cgPXc3M/R+Bbm1G73gZtWEjqnxWwuepNbegn/k2nD1le+v8WJiN6O2wYClqZmVGn3tSibkqmYGn1I+2Oi8WbB8U4jRQuVOgoNCZnHmgxdgJJPM6yqvM3NMrTWaVbjG6uwsunUdfPGdcGiP/cuUiDAzQHj2wxAflVaZLrrwKejrR//4zOFkHS26wPK5RcdYfheolcR0z1cb70L9/Ef37X6E+/QXbY0kW3dMNRw6h3r8pZcfPUfjKQGvThJbgYAsr0b/5hVnZ3vfxpM5TK9ejn/2OqSJxSMw5cwIunkV96q8z/tSTSswBvAuX0n/GYjHvaEfNnmfNtYpLnTHbamuBopKk/qBVRRUazKAKi8U8/P3/gT64a/CBnBwzWLiiCrVqPZRXUXr9CgJ5haPyvLqnG/3SL9C1h1E2i7nu7oLzp1Ef+mTcn6vpRahb/gS983foDz80OI3HJei398PVftT7brPsmsrnN78Xbc0ZF3N9+SL69W2o2+5G+a9L6lw1vQgWr0C/8Tr6TyfeNLUDvTfqI//+jD/3pBPz3AVL6T/0Orq3x3R/pYnW2myAptkwFKPEmS5Q3d46sSfLSCIfYXXTOay0ANKBVvSh3bC6Bs+GO8xq218+KpWT6/ejmkfnZlV+ASy4Hv3uYbj/zy2MLA4n68wqcJy0nbrrT9F/fBn96n+gPvaX9saTJPrALrOSrl5q3UWH1Jrbaw01Gv2fPwdPDureP0vpfLWmBv3c/zZlpWVp1NungA5dHfSRn5aEj7xFTK4NUCB34VLzEbDRok3Qni4IhazJmQOqqMSZLtC2lvFnf8ZB5RWYHLvF5Yn68B7QGs/mB1Er16PKq5LOyavlq+DsKds/5egTR82nhuolY8fivw51863oP/4O3dVpazzJoDuDcPRN4+1tZdVEbBZoZmvN9YVG9P4dqDs/mHBV1kjUqg2glDNVLe++YXzkM9S+P5JJJ+beBWYFoq1KtVjVMBQl4s+S8R31QGtqfwAVVSaXbSH60G6YNTetDUO1whgq6do3rQorLrq+FuYsQE3NGz+ee+6Hvh7jE+IS9Bt7YGDA0hQLmI1fCgozXtES/vefwdQ81D0PpHwNVVwKi5ahD++1MLLECEd95K3aiE6SSSfmOSU+Y9Np1SaoRb4sMYpL4Wr/YFVGBtD9fdDVkVxZYgRVbu0IOd3WAieOJTd7Mh5V800TV+1hS+KKh756FU7VJ1QZpWbPhxWr0a/+p2uGV+gDO02qbLYNm30+f0Zb+vWZk3B4D2rTZpP7TgO1+hY4f4bQudPWBJcAujMIbx9Erbsjo5VhQ5l0Yg7A3IXWrcyjvixpdn/GcKI8MdGhFPGoqILeHssqcKIpFrUmvQ0g5fGglq9CH33TPq+bM/WmlnnR8oQO99zzAHS0GwMlh9FtLVBfi1p7mz1DDzI8Pi78789BQaFp1EoTtXoDAL17d6R9rUTRB3fDQCjjteVDmZRiruYtNCVvFjj+xXxZrMqZR1MdmfQ1j42LSz7NoiJTh7Ao1aIP7YaqeWZodLosX21mPJ45mf614qDrI52difYsLF4B8xejf/8rx4c36EO7zZumxSmWKMqXucYhfeIYvHMIdc/9xmclTVTpDFiwlL69r1kQXWLovdvN771TJZFMVjGPGu83WvBHHvVlKUzvo12MWBdo5lbmui35hqEYEdG1wnBLt16JpFisKctSy1aazSybUi26vtbUtyf4Rq6UwnP3/XClCf1G5nOyQ9EHdppcfxJNNUnhK4OuDuutM0agtSb862dNB/bG+yy7rlpdQ+hUPfryRcuuORb64jk4ddzRVTlMUjGPTlHRVqzYggGYNt26PFfMnyWDaZb2NNIsxT7Iy7dkZR61IE3X7CmKml5sUmrvWi/mOhyGk8eG+bEkxKp1MLMS/covHWsb15cvwOl61Ptute9JYr7mNle01B2B995B3fuxCTehk0GtqQEiaT+b0Xu3R0bD3WH7c43HpBRzNb3IOJJZkDfXHQHLUiyAEcYpUyCYQTFvazXt6CmYLJkRclXWrMwP7YbZ8y1dLarlq6DhuPUlgRcazajAhcmJufLkoO7+iPlUeOxta2NKEH3ANGOpm+0Tc1UanThkX6pFa034Vz+FUj/q9nssvbaaMRPvwqW2e5zrcNh4sSxfZSppHGRSijkA8xaiT9enf52OduvKEomIY7EPApncAG2BYl/qo8IqqtJemeuWy9DwnuWdb2rFauMRX2etcMbMtVKwKFUb7oTiUkcMuLTWJsWycFl6QygmIrIy13bWmh85aNIT933cWGFYTN6GO+F0vfndtIv33oG2ZsdTLDCJxVzNXWhyl+mu2ILt1pUlRikuzahzopn9mcbA2PIqM5G9tzv1GCJNGpa3Mc9fAvnTrE+11B81HbNJtoyD8eBRH/gwHHvbuqqqRDl/xnh/2LTxGSOasrNxZR5++QUoK0fVfMCW60+NCKydNed673bIL0CtXGfbcyTK5BZzSD/VYvHKHDCboJnMmbe1pNwxB0MqWtIY8KwPvQ5zF6JmVqR8jXionBxYdhP63cOW5qj1iaOohctS/zRz+z2QX4B+5UXLYkoEfWAneDyxnLBdqNxcU65rU0WLvnrV5P1vfr9tddneiiqYPd+2blDd24M+vNe8himZGQ03HpaK+Xe/+10ee+wxfvnLDHz8jG2Cpi7mOhQyzTYWr8xVceb8WbTWZgM0lc3PKBWREXIpplp08yXzcTndRqExUMtXm1TShUZLrqdbLpsVZxpTYFTBNNRt96Df2JORigmIpFgO7oKlNxnbCLsptbFx6MIZ071qcymfWl0DJ+sGK74sRB/eC329GR0NNx6Wifn+/fsJh8M88cQTtLW1cfGivb/galohlJWnZ4fbaW2NeYziUujpykynYGeH8ZZJJ81SVm78SVLcBI2lWCyqYhmJWm7ao60qUdT18YdRJIva9GHI8aB//ysrwpqQq8drzUQku1MsUWaU2bcyj1aizVlgy/WjRH8n7Ui16H2vmb8dC2crpINln29qa2vZsMF0Xq1YsYK6ujoqKgY/cm/bto1t27YBsHXrVvz+1KxEvV5v7NzAkuVcPX405Wtd7WilFSiaNZu8FK8RL66eWbMJAr4cRU6a152Iqx1t5jXMmTfmaxga21g0l8/C23qFkhTibXlrHyy8nhnXr0jqvETiAsDvp3n2fHKOv0vpgw8nHd9Igmcb6C2Yhv+mNXEtg5OJK3jHf6Hnj7+j9NP/j7GasJHO/3gOcqfg3/RBPBlw5euonE3P0beZMWPGuOmohO/XEIKXL9BbUIj/+hX2dLBG4iq7YSXNs+fjOXIA38c/bdm1B5ov0Vx3hGkf/0sKk3RnTOV+JXRdqy7U19eHz2d+mfPz82lqahr2802bNrFp06bY981xrE8Twe/3x84Nl89B736VK6caUvJz0I1nAOhQHjpTjCdeXDrHTGdpPXUSlWP9Lv1Q9Gmzwunw5I75GobGNhYDZZUMnDmZ9P+LvtJE+EQd6qN/kfS5icQVJbz0RgZe+w1Xzp9Lux554N3DUL2Elrb4qbBk4tK33wuvvkTL/3kaz0f+r7TiGvd5wgOwexusWE1rTy/09Nr2XFHC+dPQvd00nz2DKhj7zSOZ+xVl4HgtVM2jpcX69EeUaFzhlesYeOkXXGmoRxVZUz4Y/u2LoDU9N66j18bf+5FUVo49vciyNEteXh79/f0A9Pb2Eg6Hrbr0mKh56W2C6qgvi+XVLJlrHEqr+3MIqmIWXL5o9hGSef5D0SoWe1IsUdSK1SaddPzdtK6jO4NwoTHtFEssrvJZsGo9esdv06oGmpD33iXc1oInUykWiPmaW13RogcG4NxplM0plihqzS2gNfrwPkuuZ0bDvQaLlqEcmMQ0FpaJeXV1NXV1dQCcOXOGmTNnWnXpsYm4xaW8CWqxL0uMTA52DrSAUumPvSuvgoEQNF9K6jR9aBfMX4yaYfP/96LlMGVK+iWKJ83vaCr15WPhuecB6O5C7/ydZdcciT64y/jP37DWtucYibLL17zpnHEWnZshH5PKOXDdLOu6QU/XQ9M512x8RrFMzNeuXcuuXbt4+umn2bt3L6tXr7bq0mOiCqaZ/6RUN0GDAfB6U+qcHJfCIrOhmImKlkCr8bVIs7xLRSpaktkE1ZcuQGNDRkZkqdwpsOTGtMVc19ea//P5iy2KDNT8xbDkBvQf/gMdumrZdaPo0FX0G3uY+r73o6ZmsASuNNo4ZPHKPDJYJmMrc6VMKed776A7gmlfT+/dDrlTbNvwTxXLxLygoIDHH3+cRYsW8fjjj1NQYLFAjoGauzD1WvOOdpheYvkGjPJ4TOomAytz3daSdooFgOsiI+SSKE/Uh3YD9qdYoqjlq+DyBfSVpokPHgN94piph7e449Bzz/2m8coOe9zaN6G7k7xb/8T6a49HSSl4PNZXtDSeNJYXdpmExUGtqYFwGP1WeqkW3RlEH9iFWrnOEodHK7G0zrywsJCamhpKSkqsvOz4zFsIbc0puRTqjnbrfMxHkqnBzoHW9MoSI6iCaSbXn8zK/NDrsGApypeZWYtqeXT6UGqrc93fB6dPJOxfnhTLV8OCpegX/j/L28f1gZ0wbTpTbnqfpdedCOXJMV2ylq/MT0LVfHP9TDG72pQyp5hq0Z1Bwr/6KeF/eBi6O1F3/Bdr47OASdsBGiWtTtBgwPp8eZRMdYEG0uv+HEZF4oZbuukcnDuV2Snk11WC/7rUUy2n6s0AAYs2P4eilMLzmf8KOkz4B//TbPJZgO7rRb+1H7WmxnRlZhpfGdrClbkOh+HsqYylWKIopczQimNvJ2UBMlTE9csvoG68Gc/j30ItTq4MNxNMejFnTrXxvE7FDrejPWEv62RRGRBzffUqdAbN6skCVLkx3EqkbT5WxZLBvKFSyqRa6t5JKTet62vNFzY1eaiyctSn/tp0HL70c0uuqY8chP6+zDUKjUCV+q3dAG1ugp5u83ebYdSaW2BgAP32/gmPHUvEPY/8HWpW6rNt7cSZYXUWovLyjYVrkitzrbVZmVvtyxKl2GdGjA0MxG1MsYSANWWJMcqrzOzSYGDC6hh9aLdx7rMgxZMMasVq9B9fMVUpS25I6lx94qgZNG1jw41n3e2Ej76F/s3z6KU3opKMcST6wE7zu2Rh9U1S+Pzw5j601pbsLekzmd38HMa8RWa26eG9MIa5l+4Iov/wa/T230B/r/Fd+eDHXSvgQ5n8K3MiqZZkK1p6eyB01foa8yjFpaD14IxRO4iMplMWibmqiGxITZBq0RfPwvkzmU2xRFl6I+TkJJ1q0eEBOFlnaUniWKhPPgJlFYR/+HVT154iursT3n0Dtfb9mc0vD6W0zPydRCdypcvZk5DjNeWCGcakWm6B2sPonuE9AbojSPjFZwj/vw+jX/mlWYlvcfdKfCRZIebMWwjtrcYKNlFiDUM2pVlK7B/sHHu9Vq2Oy6OGW2fHf95Dr4NSqDUbrHneJFB5BbBwWfJ583OnzRu4Dfnykai8fDyP/B10tBN++lspuz3qw3shFHIsxQLW15rrMw1QOduZ/D+Y39lQyKSvmEDEHXjDSYdJn2YBszLXYFbnKxMUtkjDkG3uc9Eu0EAbzLXnKbCo+zNG6QwzsWgCK1x9aLfpfrPqeZNELV+FfvEZdKA14c3fmLlWhtIVau4C1AN/jn7+R+gdv0Xd+cGkr6EP7DRGTvMW2RBhgsTEvDntOLTW0HgSleGqnGFUL4ViH3rPdsLnzwxPp9z38Ukn4EPJjpX57GpQnuTy5tGPjbblzDPQBdreaup1Lap3jY2QG6fWXJ9vNO3wTqRYIsRKFI++mfhJ9UfBV5axMkqIuCrecDP6+R+jz51K6lzd3gZ176DW3mabEVVCRBuHrKhoaWs2G/ZzHciXR1Aej6lqOfrmpF+JjyQrxFxNnQqVs5PqBLXNlyVKdMVv5yzQSMOQlX/sqqJq3Jy5PrTbDK9dbe9whHGZPd+8WSaYatFam2EUGd5EVErh+fTfwrRCwt//n0lNutdvvA467GiKBTBpSG+uNbXm0c7P2ZmvZBmKuvsjqLs+QraIeJSsEHOImG6dOZF4fjIYMP+m4LaYUDzeXCicbn/O3Grb1fIqaL2C7u0Z/XxaGzFfvNzR4bVKKdSyleijb5mNzYm40mT+HzKQLx+JKirB85ePQtM59PM/TPg8fWCnqbxxePNNKWVSLRaszHXjSeMjNHu+BZGljpoxE8/H/iJrRDxK1og5cxeZ1Emiv3TBdiiYZkTXLop9aDsHOwdaLc9bx0bIXbow+ofnzxiDIQdTLDGWrzZTohLoLxjMl9vQ+ZkAatlK1N33o3f+LqERZrrlsqm8WXtrBqJLgFK/JYOddWMDlFelbWEsxCdrxFxF83CJplrsbOWPUlxqmz+L1jqWZrGUCiPm8TpBB1Msma9iGYlatso0iyWSajlxFAoKY6/NCdTmh2D+YsLPfHvCdn99YJc5x+kUSwRl0cqcxgbHUyzZTNaIOVXzTP1xgpuguqPdvlb+CLZ2gXZ1mPrfUovTLGUVxlxpRHmiSbG8DktvyMz8yQlQ04tg3qKEfFp0/VFTfeNx7tddeb14/u8vQjhM+If/a9x2f31gp7EVdotXdmkZBFrTsijQwYB5Q8iU7e01SNaIuZoyFSrnJL4JGgzYt/kZpdgHwTZLp8rHsLhhKIrKzQV/+eiV+bnTcOl8xhwSE0EtXwUNx8f12tDBgInbBXMa1cwK0+5/4hj6pV/EPUZfPGs8b1yyKgdMzjwcTu9TZoZtb69FskbMAdS8RYlvgna0o+wqS4xSXGqm43QnbuyTMFa38g+lompUrbk+tBs8HtQq51MsUdTy1aDDcOytsQ864Wy+fCSedbejaj5g2v3fGz01SR/YZRqy3LAvEUENrTVPEX3WiDmSZrGNrBJz5i406YcJpuXogQFznM1plpi/iQ2boLFxcTZ4o6jyKrh0PvaxOlbFsvRG24zJUmL+YiiYNm7eXNcfg9wpjtY2j8S0+5cT/tHwdn+ttUmxLF5hnROmFVhRa37mJPivs9UX51onq8Q8NhO0cYIKh86g8U2xOfcbK9+zYxM0kmZJe1xcPCqqzCeKlsib4tkGuHzRVatFAJWTg7p+Jbr28JifxnR9rck/21m1lCSxdv9ggPDT3x6MvfEkXL7grhQLDO8CTRHdeBIkxWIrWSXmVM4Fr3fivHmkYUhlImcO9gypCLRExsVZL1Kx8sSLJtWiD+6GnBzUqvWWP1faLF9l3tguNI76ke7tgbMNtviXp0u03Z+39qF3vAxENj5zvGYqjpvInwZ5+SlXtOjuLrjShHLA9vZaIqvEXOXmwqx5E1e02DXIeSQ2rszNuDibPoqXR8sTzw6mWK6/CVVoT4NVOsRa++OlWhreg3A4452fiaI+8CFYscb4t5w9Zd40l69CTZvudGjDUEqlV2t+1lgZyOanvWSVmMOgHe54m6Dabl+WaCx5+TA1357yxIANNeYR1LRCk4K6eM5McGq+5LoUSxTl88OsuXFLFPWJo6A8sGCpA5FNjPJ48PzFF6BgGuFvfA3amt2XYoni86ecZtHRtKeUJdpK1ok58xaaAQtXLo59TKyVv8T+eOyqNQ+02jsYotyMkNOHdpuP/itdmGKJoJavgvpadF/vsMd1/VGYPQ+Vn5nh4qmgikrwfOZR08Q2ZYqzjoLjoHxlqTcONTZAiQ9V5JwFxLVA1ol5dCbouHnzjoAxyM/EdO3iEstz5jo6LMBGC1pVERkhd+h1WLbS1VUIavlqs2H73juxx3QoBA3vuTJfPhK1bBXqEw+jNn/KfJpzI6V+CAbMqMIk0Y0npSQxA2SdmFM5x7i8jZc3D5ruz0xYi6pin/Ur8+j17CxfK68y9fEtl13VKBSXRctgytThefOzDWZ2pkvz5SPxfOBDeO76U6fDGJtoRUuSq3Pd1wcXzw3abQi2kXVirrxemD1/3JW57mi3PV8eww5/lkiNuZ3DIWIVLTle1Mp1tj2PFajcKbDkhmF586i5lhNOidmIKk1NzDl/2lj5yuan7WSdmEOk3rzxJDocjn9ABnxZYhT7oLdnVD43LWLj4mxcmVeYEXIsX4UqcG+KJYpavhouX0RfNnsluv4olJW7q/lmMhMZ6qGT3ASNbX5KWaLtZKWYM3ehmfd4OY6NK0AwYH+NeRQbyhO1na38UUpnoGo+gOfu++17DgtRKyIlirVvmkqmE0cnRb580hBdmSdbntjYANOmx94MBPvISjEfbxNUa202QDOUZokNdraypb+t1ewL2FiPHC2bU4vd4WkyITMroKzcpFqazpsu30mSL58MqKlTzbCVZHPmjQ0wp9rZ0XfXCFkp5lTMNrMx422C9vVCf7/9XuZRooOdrRwfF2g1K2f5A4mhlDIlinVH0HVHzGMi5tZS6k8qzaJDV+H8aen8zBBZKeYqJwdmV8ffBO3IUPdnlKLoYGfrxNyWcXFZgFq+Gvp60X/4tfn/vW6W0yFlF8nWml84a0pGZfMzI2SlmEPEDrfx5OgZkZGGoYzlzAunm5p2KytaAi22VrJMWpbeYO71lSZYeL18crEYVZpcF2jU9lYqWTJD1oo5cxdCf1/MLCpGhlr5oyiloLjEspy51jrSyi8r85GovAKIDKGQzU8b8PmhuzPusO+4nDlp7CxmVtgblwBksZhH7XBHmm7pWCt/Bn25i33WpVm6u0zOX1bmcYlWtbhlGEVWkWStuen8nO/ouL5riey9y9dVwtS80ZugsZx5SeZiKZ1hhj1YMT4uYN9QimxA3XEv6jP/1Xj0CJaiouWFCaRadHgAzp2Wzs8MkrVirjw5MKd6tB1uRzvkTzN2uZmK5ca10HLZWLKmi02zP7MFlZePZ/0dki+3g0hLf0JWuJcumsox8WTJGFkr5gBq7iJobBg+VTwYyGyKBcywgSlT0Xu2p32twYYhyZkLGaZkBiiVUJol2vmpxPY2Y6Qk5oFAgK997Wux70OhEFu3buWxxx5j+/b0Bcsy5i2Eq/1wcXAKTUZ9WSKovALU6hr0wV3oq/3pXawtA92fghAH5fWaUttEKloaG0xjW/ls+wMTgBTEvLOzk6eeeoq+vr7YY6+88grV1dU88cQTHD58mJ6eBHe7bSZuJ2gmfVmGxlKzEXq60G/tT+9CgRYonJ7RNJEgxPD5ExrsrBtPwqy55g1AyAhJi7nH4+HRRx8lP3/Qd7m2tpaaGjO3cPHixZw8OcFA5UwxswLyC4ZvgmbSl2UoS24wfwhpplp0oFVW5YJzJFBrrrWGxpOy+ZlhJnzb/P73v8+FC4OGVStWrOCjH/3osGP6+vrw+UwOt6CggPb29lHX2bZtG9u2bQNg69at+P3+1AL2epM6t3XBUvT5M8zw+9EDA1zuDFJQXklhis+fTlydd95L16+epdQDOb7Unr+lox3PzApKk4g/2XuWKSSu5HBDXB2zZtNde5gZMwbtJEbGNXD5Is3dXRQuu4kCB+N1w/2Kh11xTSjmjzzyyIQXycvLo7+/n4KCAnp7e8nLyxt1zKZNm9i0aVPs++bm1EZQ+f3+pM4NV85Fb3+JK00XTY221nR7c+lN8fnTiUuv3AC/fIaWl3+F5+6PpPQ8A82XULPmJnUPkr1nmULiSg43xBXOmwZ9vTQ3no4Nnh4Zl37rIABdvpl0OxivG+5XPNKJq7KycsyfWVLNUl1dTV1dHQCnT5+mrMxFdpfzFkLoKlxojNWYKwdy5gCqfBYsWIre82pKNec6FIqMi5NKFsEZ1IyJa831mQbweGDW3AxFJYBFYn777bfz/PPP85Of/ITz58+zaNEiKy5rCcM2QTM5yHmseDZsNG8sjSnsKwTbQGvJmQvOEfM1H0fMzzZAxWzUlKkZCkqANMR8y5Ytsa/Lysp47LHHWLJkCV/96lfxuKl9t6zcDG4+c8KUJULGSxOHota+H7y5qW2ERsfFSfen4BTRxqG2cRqHGk+K7a0DWKa6Pp+PmpoaCgoKrLqkJSilYO7C4SvzTHmZx4unoBC1aj36wB+N33MyRLo/Yx7pgpBpikogJ2fMlbkOtJqB4+KUmHFctIS2DzVvIZw/Y0Ze5eRA/jRn49mwETo74MihpM7T4ssiOIzy5Jg031i15tHOTxHzjHNtiPncRTAQQh87AoXFzru4LVsJxaWE9yaZamlrAa8XCotsCUsQEmKciUO60XiYM2d+BgMS4BoR85iD3rlTjnR/jkTl5KDW3QHvHBrM4ydCoAWKfWIiJTiK8vnHHOysG0/CzErjLS9klGtDzH1lZuIPOLr5ORRVsxEGBtAHdiZ8jo7M/hQER/GVQVsLOhwe/bPGBun8dIhrQsyjm6AAysHNz6GoWXPNxuyeVxM/KdAq1reC8/j8MBAanA0QQXd1GKtnqWRxhGtCzCGSNwdXpFmiqA0bjUXvudMTHivj4gS3oMaqNW+MzvwUMXeCa0fMo3lzBxuGRqLedxvkeNGJbIT2dBuzf1mZC04T9RUaUWse9TBntqRZnOCaEXOql8CUKaiKKqcjiaGmF8ENN6P37Rg+QCMeMpRCcAulpqV/VEVLYwP4yszvtZBxrhkxV8WleP7XM3DT+5wOZRieWzaaZqbaw+MfGJDuT8ElFE6H3Cmjas1140nJlzvINSPmEJn447ayvhVroLBowvZ+He3+lDSL4DBKqVG+5rq3By5dkGYhB7mmxNyNKG8uat3t6Lf3o7s6xz6wTdIsgovw+YcPdj53CrQWMXcQEXMXoDZshFAIfXDX2AcFWmHadHGiE1yB8pUNX5mfiXZ+SprFKUTM3cCcapg1d9yacy1liYKb8PmhvW1w477xpCn7ld9RxxAxdwFKKdMReuo4uulc/IPaRMwFF1HqBx2OOXnqxgaYu8B9e1LXECLmLkGtuwM8nrE3QqX7U3ARakitue7vg4uNqNmSYnESEXOXoIpLYflqU3MeHl5zrgcGTPmilCUKbmFIrXmosQEGBsSTxWFEzF2Ep2ajqd2tOzL8B8GA+UgrK3PBLcRW5s1cbThuvpZKFkcRMXcTN70PCqaNTrVEG4ZEzAWXoPILIL8AWpsJNRw3A1/81zkd1jWN1+kAhEFU7hTU2lvRe7eje7rNHwwM1piXygao4CJKTa351a4OmFMtm58OIytzl6E2bIT+fvQbr8ce0+LLIrgRXxk0XyJ05oQ4JboAEXO3Ub0Erps1vOY80AI5Xih0j32vICif38zW7e+XZiEXIGLuMmI15/VH0VeazINtrVBc6vzsUkEYSqkftAZkgLMbEHVwIWr9naBUzOdct8u4OMGFRCtapkyF8lnOxiKImLsR5fPD0hvRe18zcxal+1NwIdGJQ7nzFqI8OQ5HI4iYuxRVsxGaL0H9UQi0SFmi4D58pnHIW73E4UAEEDF3LWrVBsjLJ/zaS9DbI2kWwX3MKIMlN5C34Q6nIxEQMXctamoeas0tcHiveaBY0iyCu1DeXHK+9N+YcuPNTociIGLualTNxsFqAVmZC4IwDiLmbmbhssEWacmZC4IwDiLmLkZ5PKhb74KpeZIzFwRhXMSbxeWoex5A1XxAxsUJgjAuIuYuR3k8UmMuCMKESJpFEAQhCxAxFwRByAJEzAVBELKApHLm3d3dfPOb32RgYIC8vDweffRRvF4v3/3udzl//jyrVq3igQcesCtWQRAEYQySWpnv2rWL++67j69+9auUlJTw1ltvsX//fsLhME888QRtbW1cvHjRrlgFQRCEMUhqZX733XfHvg4GgxQVFbF79242bNgAwIoVK6irq6OiomLUudu2bWPbtm0AbN26Fb/fn1rAXm/K59qJW+MC98YmcSWHxJUc11pc44r597//fS5cuBD7fsWKFXz0ox/l+PHjdHV1sXjxYl599VV8PlM6l5+fT1NTU9xrbdq0iU2bNsW+b25uTilgv9+f8rl24ta4wL2xSVzJIXElRzbGVVlZOebPxhXzRx55ZNRjnZ2d/PjHP+aLX/wiAHl5efT39wPQ29tLOBxOOyg7z7UTt8YF7o1N4koOiSs5rqW4ksqZh0IhvvGNb/Dggw9SVma8jKurq6mrqwPgzJkzzJw50/Igh/IP//APtl4/VdwaF7g3NokrOSSu5LjW4kpKzLdv305DQwMvvvgiW7ZsYc+ePaxdu5Zdu3bx9NNPs3fvXlavXm1LoIIgCMLYJLUBetddd3HXXXeNevzxxx/nyJEjbN68mYKCAsuCEwRBEBLDEm+WwsJCampqrLjUhAzdRHUTbo0L3BubxJUcEldyXGtxKa0j0w8EQRCESYu08wuCIGQBWSfmoVCIHTt2OB3GpEHuV2LIfUqNYDDIgQMHnA5j0pDO/XLMzzyez8sPfvCDYR4vqXjB7Nixg5UrVwIQCAT4+te/zj/90z858AqtJRP3C+CHP/whK1eu5OabJ+eQXqd+r86dO8fPfvYzvvzlL2fy5VpGOvdtvL+z7du3c88998S+b2xs5JlnnuGxxx7L5MuzHLvvV0NDA8899xx9fX2sW7eOD33oQxPG5NjKfKTPy+uvvz7K4yVZL5je3l6CwSB+v5/Ozk6eeuop+vr6nHqJlmL3/QI4duwYgUBg0go5OPN71dTUxLPPPkt3d7cTL9kSUr1v4/2dtbS0UFBQQF5eHgBaa5555hlCoVCmX57l2H2/fvKTn/C5z32Of/7nf2b//v1cvnx5wpgcE/O7776bG2+8ETAfLXbt2jXK42XkMUVFRdTW1o46Lsr27dvZuHEjAB6Ph0cffZT8/PxMvizbsPt+hUIhvve971FWVsbBgwcz+dIsxYnfq/z8/FhH9GQl1fs23t/Za6+9xh133DHs++XLl9v/YjKA3fers7MTv9+PUorCwsKEFgqO58yjPi8zZswY5vHS3t4+6pjFixfT19cX97iuri5CoRAlJSUAFBQUZGXNu133a+fOnVRVVbF582ZOnDjByy+/nNkXZjGZ/L0qLi4mNzc3A6/KfpK9b2P9nTU1NTFjxgymTJkCQEdHB7t27UooXTCZsOt+LVmyhFdeeYXdu3dz5coV5s6dO2Esjop51Oflc5/73JgeL0OPgbG9YF599dXY6ilbsfN+nTp1ik2bNlFSUsKtt95KbW1tJl+apcjvVWqkct/GYufOndx2222x75977jkefPBBvN7sGTts5/165JFHqKys5JVXXmHz5s0opSaMxzExH+nzEs/jJVEvmPb2drxeL4WFhU69HNux+36Vl5dz6dIlABoaGlxpHZoI8nuVGqnet3g0NjZSWVlJTk5O7LFjx47x3HPPsWXLFk6fPs3Pf/5z21+Tndh9vzweT8yM69Zbb00oJsfEfKTPi9Z6lMdLol4wQ3Oa2Yrd92vjxo3U1tby+OOP87vf/Y4Pf/jDDr3S9JDfq9RI9b7FY8+ePaM6wp988km2bNnCli1bmDdvHp/4xCcy8bJsw+77BfDzn/+chx56KKFVObisA7Szs5MjR46wbNmyWI4ykePa29spLi7OXKAuQe5XYsh9So1E79tIrtX75vT9cpWYC4IgCKnheDWLIAiCkD4i5oIgCFmAiLkgCEIWIGIuXHN8/vOfT6g9WhAmEyLmgjCCrq4ufvOb3zgdhiAkhYi5IIygq6uL3/72t06HIQhJkT29tYIwBuFwmB/96EccPHiQFStWxFz7nn/+eV599VU8Hg+f/OQnue2223jyySc5cuQInZ2dPPzww8ybN4+vfOUrgDFC+vWvf01/fz8PPPCAa8eSCdcmIuZC1rNv3z5OnTrFt7/9bQ4fPszu3btpbm7m2LFjPPnkk3R3d/P3f//33HbbbXzhC1/g8uXL/OM//iNPPfVU7BqNjY289NJL/Ou//isDAwN86Utf4uabb06qOUQQ7ETEXMh6jh8/zvr165kyZQrr169n2rRp+P1+Pv3pT/PSSy9RW1s7zOUuHrW1tVy+fJkvfOELAPT393PhwgURc8E1iJgLWc/IJmelFC0tLXznO9/hYx/7GLfffjuf//znJ7zGbbfdxsMPPwxAT09P1tjeCtmBbIAKWc/ChQs5cOAAV69e5eDBg3R2dlJfX8+CBQu49dZbefPNN4cdP336dDo6Oujr66Ovr4/+/n5WrFjBW2+9RSAQoKenh7/7u7/j3LlzDr0iQRiNrMyFrOeWW27h3Xff5a//+q+ZP38+JSUlrF+/nj/+8Y989rOfZd26deTl5XHhwgUqKyvJz89n8+bN/M3f/A1aa5544gnmzJnDAw88wFe+8hXC4TD33nsv8+bNc/qlCUIMMdoSBEHIAiTNIgiCkAWImAuCIGQBIuaCIAhZgIi5IAhCFiBiLgiCkAWImAuCIGQBIuaCIAhZgIi5IAhCFvD/AzZhayhVnSCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-8.356043365123703, 2.9074008272298893e-13, 1, 19, {'1%': -3.8326031418574136, '5%': -3.0312271701414204, '10%': -2.655519584487535}, 76.75838755149513)\n",
      "一阶差分序列的白噪声检验结果为：    lb_stat  lb_pvalue\n",
      "1  6.04427   0.013952\n"
     ]
    }
   ],
   "source": [
    "d3_sale=d2_sale.diff(periods=1, axis=0).dropna()\n",
    "\n",
    "#时序图\n",
    "plt.figure(figsize=(10,5))\n",
    "d3_sale.plot()\n",
    "plt.show()\n",
    "\n",
    "# plot_acf(d3_sale,lags=8).show()\n",
    "# plot_pacf(d3_sale,lags=3).show()\n",
    "print('原始序列的ADF检验结果为：',ADF(d3_sale.sale))\n",
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d3_sale,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gothic-litigation",
   "metadata": {},
   "source": [
    "平稳性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "random-balance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果为： (-4.794166649232149, 5.5800720586148674e-05, 0, 10, {'1%': -4.331573, '5%': -3.23295, '10%': -2.7487}, 66.51192234482832)\n"
     ]
    }
   ],
   "source": [
    "#方法：单位根检验\n",
    "\n",
    "print('原始序列的ADF检验结果为：',ADF(d1_sale.sale))\n",
    "\n",
    "# 解读：P值大于显著性水平α（0.05），接受原假设（非平稳序列），说明原始序列是非平稳序列。\n",
    "# 也可以判断t值（第一个值），如果小于第五个值，小于1%的值=严格拒绝原假设，小于百分五的值=拒绝原假设，依次递减"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composite-savage",
   "metadata": {},
   "source": [
    "白噪声检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "given-ceremony",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一阶差分序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  3.287155   0.069824\n"
     ]
    }
   ],
   "source": [
    "print('一阶差分序列的白噪声检验结果为：',acorr_ljungbox(d1_sale,lags=1))#返回统计量、P值\n",
    "\n",
    "#解读：p值小于0.05，拒绝原假设（纯随机序列），说明一阶差分序列是非白噪声。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sticky-steam",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "(p, q) =(sm.tsa.arma_order_select_ic(d3_sale,max_ar=3,max_ma=3,ic='aic')['aic_min_order'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "sweet-static",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(p,q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overall-brunswick",
   "metadata": {},
   "source": [
    "### 五、建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "serious-endorsement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n"
     ]
    }
   ],
   "source": [
    "#创建模型\n",
    "model=ARIMA(sale,order=(p,3,q)).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "comprehensive-cradle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>sale</td>       <th>  No. Observations:  </th>   <td>24</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 3, 2)</td>  <th>  Log Likelihood     </th> <td>-66.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 19 Nov 2021</td> <th>  AIC                </th> <td>138.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>10:59:11</td>     <th>  BIC                </th> <td>141.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-2019</td>    <th>  HQIC               </th> <td>139.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 09-01-2021</td>   <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -1.9785</td> <td>   72.395</td> <td>   -0.027</td> <td> 0.978</td> <td> -143.871</td> <td>  139.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>    0.9990</td> <td>   73.035</td> <td>    0.014</td> <td> 0.989</td> <td> -142.147</td> <td>  144.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   21.3571</td> <td> 1562.611</td> <td>    0.014</td> <td> 0.989</td> <td>-3041.305</td> <td> 3084.019</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>2.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.93</td> <th>  Prob(JB):          </th> <td>0.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.31</td> <th>  Skew:              </th> <td>0.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.15</td> <th>  Kurtosis:          </th> <td>3.06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   sale   No. Observations:                   24\n",
       "Model:                 ARIMA(0, 3, 2)   Log Likelihood                 -66.318\n",
       "Date:                Fri, 19 Nov 2021   AIC                            138.636\n",
       "Time:                        10:59:11   BIC                            141.770\n",
       "Sample:                    10-01-2019   HQIC                           139.316\n",
       "                         - 09-01-2021                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -1.9785     72.395     -0.027      0.978    -143.871     139.914\n",
       "ma.L2          0.9990     73.035      0.014      0.989    -142.147     144.145\n",
       "sigma2        21.3571   1562.611      0.014      0.989   -3041.305    3084.019\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 2.79\n",
       "Prob(Q):                              0.93   Prob(JB):                         0.25\n",
       "Heteroskedasticity (H):               0.31   Skew:                             0.89\n",
       "Prob(H) (two-sided):                  0.15   Kurtosis:                         3.06\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看模型报告\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "infrared-silly",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "未来7月的销量预测：\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2021-10-01    13.343174\n",
       "2021-11-01    13.042329\n",
       "2021-12-01    12.717465\n",
       "2022-01-01    12.368582\n",
       "2022-02-01    11.995681\n",
       "2022-03-01    11.598760\n",
       "2022-04-01    11.177821\n",
       "Freq: MS, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预测\n",
    "print('未来7月的销量预测：')\n",
    "forecast = model.forecast(7) #预测、标准差、置信区间\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "sorted-yesterday",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AGgZWPgAAAABxzlor++Zz0vyZnnPM4BNkjjw58jfv1lMyRrK2YlxL5su02SPy9wQAACGrV6/W9u3bQ/u7Vif06tVLzz33nLp06VLhnOquYNilug2dExMT9eSTT6p9+/Zq166dUlNTlZaWJp/P+33p4uJiZWdnSyrt+fDTTz9Veo9AIKCSkhIVFxcrNzdXqamp1YoRQM2RfAAAAADinP16oux3n3tPyN5X5uxLo1ICyaRlSO33ktasqDi4dL406KiI3xMAAPxh/PjxFY6deuqpevjhhz0fxFd3BUN+fr7mzJmjTz75JHSssgTCLn6/X6eddlq17rW7lJSUsOe2bdu2VvcCUD0kHwAAAIA4ZudMl33rBe8J7TrId8VomYTo/WhgsrJlXZIPbr0gAABAZE2dOrXc/hVXXKFbbrml0pcOCgsLw7r24sWLddppp2nHjh0Vxlq1auV6zqZNmzR9+nT5/X4lJiYqPT1dGRkZSkxMDOueZRMjwWBQS5cuDes8qXQVRFFRkQoLC0P/HzRokNLT08O+BoDwkXwAAAAA4pRd+5uc5+6XrEfphIxM+a66tXR1QjRl7SN982nF47+vlc3ZLtOkWXTvDwBAI/bCCy/o1FNP1bJlyzRq1Chdd911VZ4TbvKhR48eOuywwzRx4sRyx40xnisa5s6dq4svvjis61dl69atOuKII2p1je+++07du3ePSDwAyiP5AAAAAMQhm7NdzrixUmGB+wR/gnx/uzEmPRdM916q2PHh/y2dL/UfGPUYAABorJo1a6ZHHnlEkyZNCivxIJX2i0pLS5NUWhqpMueee24o+dCqVSsdeuihOvvss3X44Ye7zjfGKCUlRenp6UpLSwutgAinTFNtlZSUVFj5ACB6SD4AAAAAccaWFMt56h5py0bPOeaCK2V69I5JPKZ5S6lVW2nz7xXG7JIFMiQfAACIqgEDBmjAgAFhz999JUNlBg4cqHHjxmnfffdVt27dqpw/ePBgLVu2LOzrA2i4SD4AAAAAccRaK/vKOGnZQs855vgz5Ytxo2eT1UvWNfkwL6ZxAACAyEpMTNQZZ5xR12EAqIeiv54JAAAAQMzYTybI/vyd94R+B8ucfkHsAtqley/346uXy3qVhgIAAADQYJF8AAAAAOKEM/V72Q/He0/o1FW+S0bJxKCm8u5M1j7uA44jLV8U22AAAAAARB3JBwAAACAO2BVLZF961HtC0xbyjbxVJjklZjGV0669lNHEdcgumR/jYAAAAABEG8kHAAAAoIGzWzfJefIuqaTYfUJSknwjby5t/FxHjDGepZfsUpIPAAAAQLwh+QAAAAA0YLawQM64u6Qd2zzn+C4aJbNXVgyjcmeyst0Hli+SDQRiGwwAAACAqCL5AAAAADRQ1gnK+fdD0poVnnPM6RfIDBgYw6i8efZ9KC6SVi+PbTAAAAAAoorkAwAAANBA2fdelWb94jluDhkic8KwGEZUhY5dpaRk1yG7ZF6MgwEAAAAQTSQfAAAAgAbI+d8k2S/e957QPVvmgpGlvRbqCZOQIHXt6TpmlyyIcTQAAAAAoonkAwAAANDA2EVzZMc/7T2hVVv5Rtwkk5gYu6DCZDyaTmvpfFlrYxsMAAAAgKgh+QAAAAA0IPb3dXKeulcKBt0npKbJd9WtMplNYxtYmEyWR/JhZ460YW1sgwEAAAAQNSQfAAAAgAbC5u2UM26slL/TfYLPJ99lN8js2Sm2gVVH156Sz/3HEPo+AAAAAPGD5AMAAAA82WBQzmfvKjjqAgWvHCbn+Qdlt2yq67AaJRsIyHnmX9Lv3qsDzJ8vlendP4ZRVZ9JSS1tPO1m6fzYBgMAAAAgakg+AAAAwJP97jPZ916RcndIxcWyv0yWc+8/ZFevqOvQGhVrrewbz0oLZ3vOMUNOkm/ISTGMqua8Si/ZJSQfAAAAgHhB8gEAAACurBOUnfhWxYEd2+Q8cKPsormxD6qRsl99JDv5C+8J++wnc/YlsQuoljz7Pmz+XXbbltgGAwAAACAqSD4AAADA3YolpSse3BTky3n0dtkZP8Q2pkbIzpoqO+FF7wl7dCzt8+D3xy6o2uqe7TlkKb0EAAAAxAWSDwAAAHBl50yrfEKgRM4z98uZ/HlsAmqE7JoVcp5/ULLWfUJGE/muulUmLT22gdWSadJcatvefZDSSwAAAEBcIPkAAAAAV3b21DAmObKvPSVn4puyXg/IUSM2Z5uccXdJRQXuExIS5Btxk0zrdrENLEKMx+oH+j4AAAAA8YHkAwAAACqw27ZI1WgqbT/8j+wbz8o6wShG1XjY4iI5T94jbd3kOcdceJV374SGIGsf9+NrV8rm58U2FgAAAAARR/IBAAAAFVRZcsntnG8+lX3+IdmSkihE1HhYx5F9ZZy0fJHnHHPiWfIdMiSGUUWeZ+LEWmnZwtgGAwAAACDiSD4AAACgAs/kQ1Jy5edN+17OuDGyhflRiCr+2Z05cp64S/aXyd6TBgyUGXpu7IKKltbtpKbNXYfsknkxDgYAAABApJF8AAAAQDm2pERaMMt1zAw5SeacyyRjvC+wYJacB26WzdkenQDjlF22UM7Ya6XKVp107i7fX/8u42v4H+ONMTLd3Vc/2KX0fQAAoC4UFxdrx44dof8cx6nrkAA0YAl1HQAAAADqmcVzpaJC1yHTd3+ZHr3lZDaTfeFhKRhwv8aqZXLuGy3ftXc22IbIsWKtlf3yQ9n3XpGClfTMaNZSvpE3yyRXvvqkQcnqJU2fUvH4iiWyJSUyiYmxjwkAgEbs/fff16hRo0L7P//8szp06FCHEVX01VdfadWqVaH9Cy+8UH6/vw4jahxefPFFDRs2TE2aNKnVdaZOnapXX31VktSmTRvdeuutkQgP9RTJBwAAAJTjWXIpLV3qli1J8h1wqGx6hpyn7pWKCtznb1xfmoC45g6Zjl2iFG3DZvN2ynn5MWnmz5VPTEqW76pbZJq1jE1gMWKyesm6DQRKpN+WSB4rIwAAQHSkp6dXul8fvPPOO/roo48kla6kHD58eN0G1AhMmTJFt956qx588EENHz5cF154odq1q9kLRhs3btR7770nSerbt28kw9T++++vrVu3RvSabmbPnq2MjIyo3ycekHwAAABAiLVWdvZU1zGzT3+ZMm+VmV795Lv+bjmP3Snl7nC/4I5tch64Ub4rb5Hp2TsaITdYduUSOc/cJ23ZWPnE5FT5rhgt06lbbAKLpQ57SSmpUmHFBJZdMt+zLBMAAIiO5N1WWO6+Xx+0bt06tJ2WliZTWTnQBuLJJ5+Uta6vZHgqu9ojWNnq2f/35z//Wa1atap2bFLpqgdJ2rFjhx577DGlpaVp5MiRnvOLioo0Z84c7b///hXG0tLSQtupqamu50+fPl1du3ZV8+bu/cG8JCQkqKioqFrnVJcxpl4m5eorkg8AAAD4w4a10qYN7mN9K/7wYDp3l2/0fXIeuc37IXpBvpxHb5fv0utk+g+MYLANk7VW9ptPZN9+UQp4lK3apX3n0sRDu/pV7iBSjM8vddtbmvdrhTG7ZL50Qh0EBQBAI5aQkFDpfn2w1157hbbj5e3zBx98UMXFxVG9x8knn1yj5MPy5cv15ZdfhvaPOOIIXXnlla5zHcfRp59+qoceekhr1qzR119/rY4dO5abUzahlehSYtNaq6uvvlqbNm3S0KFDNXToUB166KFhxVo2KfD999+rS5fIrL4OBALq3LmzpNKESTwkvGKl/n0HAQAAQJ2xc9xXPcgYmX0GuA+13VO+f94v57E7pDUr3c8PlMh55n6Z86+Q7/DjIxJrQ2QL8mVfGSfr1udgN+bQY2T+fFl89XhwYbr3knVJPmjZAlnHiYvm2gAANBS7906oj8mHsg+Y3R5eR0L79u1rfY2HH35YZ599dlhzk5KSop58aNq0aY3Ou/fee0MrK5o1a6ZHH33U8+H75s2bNXr0aG3fvl2SdMMNN+iNN94oN6fs15jb19e3336rlStXSpL+85//qFmzZmEnH2LR+yMpKSnq94gn9e87CAAAAOqMne3R76FrT5lM7+ZyplkL+a6/V86Td0mL53lc3JF97Sk5OdtlTjq70b0xZFctl/PsfdLG9ZVPTEqWOe9v8g08MjaB1TGTtY9734f8PGndqtLSTAAAICZ8uyX9d9+PpNzcXOXk5KhJkyZKSkpSYmJiVO8XCARUUlKinTt3qrCwsMIb+XWp7APtt99+WwMHVr1auGzipaSkpML49OnTdeqpp0oqLRVUk0bRU6dO1aeffhrav/3229WmTZvQfiAQUGFhoQoLC9WqVSu1adNGY8aM0dVXXy1Jmjx5st56661ySZiqElpPPPFEaLtLly76xz/+EXa8ZX9PNmzYELHkWdmyVvUxIVef8bsFAAAASZLNz5OWzncdM30qllyqMCctXb5r75Tz/IPSrz953+fD/0g526U/X1padifOWWtl//eF7BvPlzZSrsweHeW7fLRM+06xCa4+6JIl+ROkYMUSVHbJfBmSDwAA1Mj27duVl5en9PR0JScnKzExscoHp9V5OcRaq2AwGHqon5eXp/z8fHXt2jWs8ydNmhR6SF1Ta9asqdEqhb322ktTplS9ErU2qpNIiXZvjdTU1GqvCggEArrtttvKHbv55pt10003yXEclZSUyHEcSVKHDh30888/S5LOPPNMvfvuu/ruu+8kSWPGjNHRRx+tli1bVnnPKVOm6Kef/vg5YuzYsTX+vRk2bFiNzqtKY3uBqrbqffJh586duuGGG7R582aNGDFCgwcPrnT+t99+q6eeeqrW950wYUKtrxGOkpISXXjhhWE1htmladOmev7556MYFQAAaJQWzJQ8PpOEk3yQJJOYJN8Vo2XHPyM7+QvPefabT6XcHOmiv8tEabl8fWALC2Rff0r25++qnGsOHiJz3hUyKe6N9+KVSUqW9uouLVtYcXDpfGnIibEPCgCAOPD+++/rlltuqdU1avJg/7fffmvwb4cvW7as2ud88803uuSSSySVPrs76qijwj432uWCalIq6OGHH9bs2bPLHcvPzw/r3LvvvltHHnmkiouLtX37dt155516/PHHKz0nGAzq9ttvD+0PGTJEQ4YMqVbMu5Ih0VSdZ7hoAMmHp556Sps3b67rMKJm1apVfNECAIB6wbPkUrOWUsfwm7UZn186f4TUpJnsxLe87zfte9m8XPlG3CiTklbdcOs9u/Y3Oc/cJ21YU/nExCSZcy4r7fHQSN+kMt2zZV2SD3bJfFlrG+3vCwAAjUFaWpoyMjKUnJwc1kPy3Nxcbdy4UVJpCZxdjYCrUlhYqIKCAm3btq3KuSkpKWFdcxfHcfTggw+G9q+++mq1aNEi7PPrW/Jh6tSp5coftWvXTk2aNJG1Vo7jhFa8FBcXq6ioqEKyqUuXLho+fLiee+45SdJHH32kf/zjH+rUyXt170MPPaQFCxZIKl01UpPEWdnyU9FqOB3t3hzxpl4nH8aPH69p0zx+CI6iWP5ws3z58pjdCwAAwIt1HNk57p+7TN/9q/35yBgjM/Q8OZlNZd98XrKuVf2lBbPkPHCzfNfcLtOkWTWjrr+cH76SHf+0VNUPJ232lO+K0TLVSO7EI5O1j+wX71cc2LZZ2rpJatmm4hgAAKhUs2bN1LdvX6Wmpio5OVkJCQny+/2VlgPaunWrpk6dGto/7rjjPOdaaxUIBEIlePLz81VQUBD258bjjz9e8+fPr3Yj5LfeekujRo2SVPpQfPLkyZ7x7dy5U5mZmeWOl5SUhJWAqI63335bCxeWvkjRqVMn/fWvf63W+dF+Flmdxtzr1q3TiBEjQi9Lt2/fXv/973+r3TPiqquu0uuvv65DDz1Ut99+e6WJB0nq2rWrBgwYoOnTp+uMM87Q3nvvXa37SaUJpmgrKiqK+j3iSb1NPkycOFEffvhhtc87/PDDw2rKsrvHH39cv/zyiySF3UE9EsomH8477zydcMIJVZ7Dm18AACDiflsq5e5wHQq35JIb35Eny8lsJvvCw641/SVJq5bJuW+0fNfeKdO6XY3vVR/YoiLZN56RnfJVlXPNAYfJXHClTGr8rfqotm7eP1zaJfNkSD4AAFBtp59+uk4//fRqnfPDDz/oT3/6U2j/xRdfjHRYIenp6RWO5ebmatSoURo4cGC1H+Dvbvz48RozZozOPfdcjRo1KvTwPDExsVzT5NoqKioqt+rhH//4R7X7FER75UO4zxK3b9+u888/X+vWrZNUGte4ceNq1Ky6RYsW+uSTT9SjR4+w5g8bNkzDhg3TN998o6ysrGrfTyqfGIjW892SkhIFg8Go/5nFi3qZfHjnnXdq3HPB5/NVeynR0qVLQ4mHpKQknXPOOTW6d02UTT507ty5RjXYAAAAasuz5FJCopS9b62u7TvgUNmMTDlP3iMVFbhP2ri+NAFxzR0NdhWAXb9GzrP3SWt/q3xiQoLMWZfIDD6Bl0r+n8loIu3ZSVq3quLgkgXSwdWr9wsAABqeTZs26fTTT9eKFSs0adIkZWdn6+CDD67RtbZu3ap7771XeXl5ev755xUMBjV27NgIR1zqjTfeCD2sz8rKqnbCZ3e//PKLcnJyqpxX9uG3W0n3mlRbGTNmjBYtWhTav+6663TQQQdV+zq7hJt4KKu6fR7Kchwn6s27pdLeF7uvqIG7epV8KCkp0Ysvvqivvip9U8wYI7/fr0DA4y25CHnttddC2yeddJJatWoV1fvtEggEtHr16tB+VcuPAAAAosWr5JJ69pZJrl7NWzcme1/5rr9bzmN3eq6w0I5tch64Ub4rb5Hp2bvW94wl5+fvZF97UiqqYql3q7alZZY6d49NYA2I6d5L1iX5YJfMq4NoAABArLVq1UpdunTRihUrFAgEdNlll+mzzz6rUdPrf/3rX9q+fbuk0vJT1157bWSD/X9FRUXleiP8/e9/r7SsVTgeeOCB2oZVY/fee68yMzP173//W8ccc4yuvvrqGl1n+/bt+v77713HVqxYEdretGmTJk6c6Hmdnj17VmsVxPTp08MPEjFRb5IPO3bs0L333hvKyvn9fl155ZV64403tGnTpqjd99dffw01M8nIyNDQoUOjdq/drVq1KpRYadq0abUa0QAAAESK3b61tOySC9PngIjdx3TuLt/o++Q8cpu0ZaP7pIJ8OY/eLt+l18n0r34pzVizJcWyb/5bdvLnVU/e72D5hl8tk5YR/cAaoqxektvv4/rVsjtzSldHAACAuGWM0SOPPKJjjjlGGzdu1JYtW/TPf/6z3EvD4Zg7d67eeOON0P4///lPtWzZMtLhSipd9bB+/XpJpW/5n3rqqVG5T6wkJyfrzjvvVNeuXVVUVKSzzz5bmZmZSk5Olt/vl9/vVzAYVDAYVElJiQoKCpSUlFShPNe6det0+eWXV3m/efPmVTrv1ltvdU0+/Oc//9G8efOUkpKi1NTU0H9l96NRFikYDCo/P195eXnKz88Pbefl5ekvf/lLjfpUxLt6k3xYs2ZNKPGQkZGhUaNGqXfv3uW+WURD2fJOp5xyitLSYldzd+nSP37Ij1T3dQAAgOqyc73fEDJ9a97vwfV6bfeU75/3y3nsDmnNSvdJgRI5z9wvc/4V8h1+fETvH0l24zo5z9wnrV5R+US/X2bYcJmjTqXMUiVMVi95tCWXli6Q+tV8yT8ANHZBx2pzfkldhxH3WqUlyu/j3/raaNWqlR555BGdd955kqSvv/5an332WVg9Une5/fbb5TiOJGm//fYLXSvSgsGgnn322dD+FVdcEZHPeuPGjdN+++1X5byEhD8e67pVjanqwX5l/vKXv+ihhx7SlClTqpzbunXrCsfc+nnUREqK+wrsH3/8Ue+9915E7hEpxx57LMkHF/Um+bBLz549dc0118Sk9NGMGTO0bNkySVJmZma1vpFFQtnaa927s/QeAADUDTt7qvtAuw5RaQBtmrWQ7/p75Tx5t7R4rkdQjuxrT8nJ2S5z0tn17qG9nT5FzsuPS4UePSx2adFavsuul6mkoTJKmZZtpBatpK2bK4zZJfNlSD4AQI1tzi/RZR9Wv/47que5oV3VNqNue3lu3bpV+fn5SktLC739nZCQUOtSQFVxHCf0RnxxcbEKCgqUn5+vtm3bVvtF38GDB+ukk07SJ598Iqk0mTB48GClpqZWee7HH3+sn376SVJpVZV//etfUfu1T5w4UatWlZaMbNeuXa17PezSrl27sF5STkxMDG2XlFRMLm7dujUi8dRE2aRBu3btNH78+LDPHTVqlGbNmlXhOrtLTExUQkKC6/8TExOj8udurQ19nTuOE/paLygoqHc/r9QX9Sb5kJSUpPPOO0+nnHJK1L8h7vL222+Htk855ZRKv6CjYVfiQypNugAAAMSaLSmR5s9yHTN9I1dyqcK109Llu/YOOc8/KP36k+c8++F/pJzt0p8vlfFFful0ddmSEtl3XpL92rs2bUjfA+S76FqZdJrRhct07yX7y+QKx+3S+XUQDQAADc/zzz+vxx9/PGLXq0m/hbLeeOMNHX744dU+7/bbb9dXX32lPfbYQ6NGjQq7ifDSpUuVlpam/Px8XXTRRerdO3p9xJ555pnQ9sUXX6ykpLpNPEXaX//6Vw0fPlzp6emaPn26zjrrLEnSiSeeqMcffzz00N0t8VE2MZKQkFCtFQFlk1VlV3eUNW7cOI0bNy7sa6Lu1JvkQ1ZWVrUaiNTW3LlzQw//U1NTdeyxx8bs3pJUXFysNWvWSFKoZtquemWbN2/Wzp071axZM7Vr104HHXSQDj74YDVpQp1bAAAQYUvmSUXub+9HuuRShesnJsl3xWjZ8c/ITv7Cc5795lMpN0e66O8yZX6QiTW7+Xc5z94vrVxS+USfT+b0C2SOPV0mRi/VxI2sXpJL8kG/LZUtKpIJ88EDAACov3JycuTz+ZSWlub5AnL79u314YcfKjs7u1q1+//+97/rkksu0fvvv68zzzyz0rnFxcXKz89XUlJStVdnTJ06VbNnz5ZU+nb+OeecU63zG4KyvWl3f6t/V18FL15Jg+qqznUKCwuj2je4rBYtWkSstFS8qzfJh1j76KOPQttHH310THs9SNLKlSsVDAYlldaIGzNmTIU5mzZt0qZNmzRnzhy99tprGjZsmE49lVrBAAAgcuycae4DqelSt+yo39/4/NL5I6QmzWQnvuU5z077XjYvV74RN8qkxPZzmyTZmT/LeelRKT+v8onNWsh32Q0yWb1iEle8Md09+j4Eg9LKxVLPPrEOCQCABqV169bab7/9lJaWpqSkJCUkJMgYU+WzpP/+97+h51RujjzyyHJvs+9uV9mlsqVoCgsLXZ+33XzzzaF6/btK5VTnWVfZONeuXev5MvPYsWMrHNtVNicQCIT6Qtx4440aOXJk2PeXpFdffTW0ffLJJ6t58+bVOn93bj0bGrJIPbusznVmz54dsdJXVXnqqac0dOjQmNyroWuUyYdVq1Zp5syZkkpXHZx00kkxj6FsyaWykpKS1Lp1axlj9Pvvv4eWLhUVFWn8+PHatGmTLrnkkliGCgAA4pid7Z58ML36yUTojaWqGGNkhp4np0kz2Teek6xH2+EFs+Q8cLN819wu06RZTGKzgYDs+6/KTvqg6sm99pPvklEymU2jHlfc2rOTlJYh5e+sMGSXzJMh+QAAQKUuuugiXXTRRdU657XXXtMXX3ivQpWk3r17a/To0bUJzVVJSYlr2Z5wWWuVn58fwYiqtnXr1lA/Ckm64IILan3NoqKiWl+jPim7oiUnJ0cPPfRQ2OeuXr26RvfMyMio0Xk1EeuX2BuyRpl8+Oyzz0LbBxxwQLllRLGyZEn55fr77ruvhg0bph49eoSyesFgUN9++60mTJigbdu2SZImTZqk/fffX/369YtIHDt27FBOTk7Y830+X4V6f36/P2Z9OtCw7L40sjpLJQEA0edsWKvgxnWuY/5+B8kfo+RDyDFDFWzaXIHnHpSCHm9/rVome98/lfCPu6LSDLssu2WTSp7+l+yyBZVPND75TztP/pPPpsxSBJRk9ZIz65eKA0sXRmwJvxs+twCo73a9pQ1E0tq1a3XXXXeF9vv3768ZM2aE9lNSUlRYWKgnn3xSRx11lPbfP3JlOVNTU5WZmam0tLQq/93dsGGD8vIqX4GalpamPfbYo9I5wWBQRUVFKi4uDv2/um/pv/3226FkQVZWVkR+T4qLi6s1f/eYjTGyXi/w1MDq1auVmpqqtLQ0JSYmVri24zgKBAIKBoOh3g+pqamuz1hzcnL08MMPRyw2L2XLQPn9/lAzcC+vvvqqbrzxRknSlVdeqZtuuqnS+YMGDdLKlSslVd4IO5Ii8cy1rivoNLrkQ35+vr7//vvQfqx7PeyyePHi0PZpp52mc889t8Icv9+vo446Sr169dJtt92mHTt2SJImTJgQseTDF198oXfeeSfs+ZmZmXrhhRfKHWvdunVEYkH8a9myZV2HAAAoI/eH/2q724AxanPk8fI3i/0LGjp5mAo7dNLmu/4hW+D+FpvduE6Be69X6zHjlNS1R1TCKJg2RVsfuk02Z0el83zNWqrlDXcrZd/o9sdoTHL2O1A73JIPyxeqdcsWMv7Y/AjD5xYA9Y3jONq2bZsCgYASEhKq9UBqj6YJeunMnlGMDpLUKj1Rfl/DKZUdDAZ17bXXaufO0hWHXbp00fXXX1+uf8GVV16phx56SMFgUCNGjNCXX36pVq1a1eq+Y8aM0f333x92b9N169aVa1p9wAEHaOrUqZKk9PR0FRUVKRAIyFqr5557Tn36RHelZNnnaMOGDau0HFW4CgsLQ9t/+tOfan293RljqhXnkUce6bmi5NNPP1XHjh0rHL/qqqt06623Sopczwe/3x923Ls3Ja9qBcX27dtD27m5uVXOL1saKzU1NSJ/7m4cx5ExRgkJCWrTpk2Df+G70SUfvvvuu1B2cs8994xq1/vK3HXXXdq8ebNycnLUv3//SufuscceuvLKK3XPPfdIkpYuXapVq1apU6dOsQgVAADEqYJfvnc9npTVq24SD/8vpd+BanPvs9p0+9VydmxzneNs26KNoy9Vq9seVkqfARG7tw0GtOP1Z5U74aUq5yb3GaCWN9wtf4va/QCO8pL32c/1uC3IV8mKpUrqvneMIwKAhs/vM2qbmVTXYaCeGTNmjH744YfQ/kMPPVThLemrr75aEyZM0OrVq7V27VpddtllmjBhQq0eLrdp0ybsuUVFRbr44otDVTsOP/xwDRs2LJR8aNGihY466ii9/PLLKigo0F//+ldNnDhR7dpFZ4Xs7NmzNX/+fEmlD/SHDRtW62sWFBTEvHRULHXs2FHTp08Pe/5pp51W7usyXGW/JoPBoA4++OCwz3311VfL9fGoSl2vJmhIGl3y4euvvw5tDx48uM7iaNasmZo1axb2/H79+ql9+/Zau3atJGnq1KkkHwAAQI05+XkqmjfDdSzlwMNiHE1FSVnZavPAC9p061UK/r7WdY7Nz9OmW69SyxvuVtrAIbW+Z3DLJm25/2YVzXX/fQkxRk3OvlhNzr1UhtI8EZeUlS0lJkklFcsPFM37leQDAAAR8P777+vpp58O7V900UU69NBDNWXKlHLzUlNTdffdd+vCCy+UJH3//fe6/vrr9cgjj0Q9RmutRo4cGXpwnZmZqUcffbRcRROptGH0F198ofXr12vVqlU644wz9P7776tt27YRj6nsqocBAwa4rgCork2bNpXbP+OMMyLyVv2WLVv03//+t0bnZmdny+fzKSMjQ0lJSdqxY4d++uknSaUvSe+7776h0kv5+fnKz8+v88oo1S1dhdhoVMmH5cuX67fffpNUmqE69NBD6zii6snOzg4lH5YvXx6Rax533HE65JBDwp7vttRn06ZNEa0rh/jh9/vLlSzYsmWLgsFgHUYEANglOG2KFHDvq1DQrZeKNm6McUQuElPk/+d9ch6+TXa1x2efkmJtuWe0dlx4pfyDT6jxrZz5M1Xy7P1SzvbKJ2Y0UeLlN6iod39t2rKlxvdD5UzXHrKL5lY4njPjZxUcclRU7snnFgD1neM4oca8gUCAN29RY5MnT9ZVV10V2s/OztbNN9+skpKScqVlpNKG0EcddZTOOussTZgwQZI0fvx4tW7dWtdff31U47zlllv04YcfhvYffPBBtW3btlyM1lplZGToscce0znnnKNgMKilS5fqxBNP1EsvvaTs7OyIxvTxxx+Hto877rhaNcveZf369aHtpk2baty4cVWes6sszy67yk6VNX369FDywVpbrVg/+uijcvs//PBDqBzUfvvtp+eff971vLLfo3ap7r3L/jqCwWDY55ZdPRLtng+1bZReGWtt6Pds48aNEen5UJeJoUaVfPj2229D27169ap1jbpYy8zMDG1v3rw5Itds2rSpmjZtWqtrBINBGl8hLMFgsMIHGQBA3XBm/uw+0LSFgu07y6kv368zmsj8427ZJ++WFld8GC1Jso4Cr4xTcPsWmZPOrtbDGOsEZSdOkJ34plTVyxTde8l32fVymresP78/8apbtuSSfHAWz1VJSUlMHrjxuQVAfcNLf4iEGTNm6JJLLgm9JZ6Wlqann366yga6Y8eO1U8//RR6oPvoo4/K7/dr1KhRUYnzX//6l1566Y8ymBdffLFOPvlkz/mDBg3S2LFjQw+QV69eraFDh+rxxx/X8ccfH5GYfv3119BLwVLk+siWfcG4R4/w+pnt/v2A7w8KldnfZcWKFZXO31LmRaIdO3ZUOb9ssiFaiYfdReKZa133jGg0yQfHccrVCxs0aFAdRlMzeXl5oe2yjWgAAACqwzqO7Fz3uqumz4B69yalSUuX79o75Dz/oPTrT57z7If/KV258OdLZXxVl0OyOdvk/PthacGsqmM47gyZ086XiVDzPFTOZPWS64/QOdulTeulNnvGOCIAABq+//3vf7r44otDz5d8Pp/GjRunrKysKs/NyMjQ888/r9NPPz30hvlDDz2k3Nxc3XLLLfJHqBSltVZjxozRc889Fzp22GGHhRoZV+Yvf/mL1q9fH1o5kJeXp0suuUTXXXedrr322lp/xv30009D2126dFH37t1rdb1d5s2bF9reZ599InLN+mTNmjVq37591O9TNvkQDAarVfHm9ddf1+uvvx72fJ7Lhq9ht8uuhvnz54ea0xhjtP/++9dxRNXPXm3dujW0nZqaGo2QAABAY7B6ueTRyNn0qfvPSG5MYpJ8V4yWOfy4SufZbz6Vff4h2SreRrKL5soZ8/eqEw9pGfKNvFW+YcNJPMRS170ljwcEdsn8GAcDAEDD9+GHH+rCCy8s92LrzTffXK1VAb1799bjjz9e7iH+c889p3PPPbfcW+Q1VVRUpGuvvbZc4iE7O1vPP/982D0Q/vnPf+qyyy4L7Vtr9eCDD+qMM87Q0qVLaxXfV199Fdo+/PDDa3Wtsn788cfQ9oEHHhix68bK7isO6kosez7s3LkzZvdq6BrNT1C7mqJIUvfu3avV7DnSfvzxR02ZMkVz587VVVddpQEDBlR5TjAY1IIFC0L7Da1kFAAAqD/s7GnuA/4Eqde+sQ2mGozPL50/QmrSTHbiW57z7LTvZfNy5Rtxo0xKWvkxx5H9/F3ZD8ZLtoqXQLr0kO/yG2RatolE+KgGk5YuddhLWu2y/H3JfGnQ0TGPCQCAhshxHN1333164oknyh0fOXKkrrjiimpf74QTTtB9992n0aNHh0r9fP/99zr88MN1ww036IILLqhRmZfly5friiuuKLcKoGvXrnr99dfLlSEPx+233660tDQ9+uijoWO//PKLjj32WJ1//vm68sorq92Meu3atVq0aFFoP1J9ZNetW6e5c/8oNXnQQQdF5Lqx8tprr2nJkiUaM2aM55xmzZqV6zFSlVdffTXUs7c6Bg4cWK4sVjj3qU7PB9RMo0g+WGv1yy+/hPbDedgfTXPmzAnF8+OPP4YVz6xZs8plp3v16hW1+AAAQHyzs6e6D/TYp8LD+vrGGCMz9Dw5TZrJvvGcd5+GBbPkPHCzfNfcLtOkmSTJ5ubIefERyaPkVLn7HH2qzJl/kUkI7y07RJ7p3kvWJfnAygcAAMLz+++/69prr9XkyZPLHf/73/+uf/zjHzW+7nnnnaekpCRdd911CgaDkqTt27frpptu0muvvaYrrrhCQ4cODWu1guM4evXVV3XvvfeWe5u8R48eeuutt9SmTc1eArn++uu1xx576Oabbw71cCoqKtILL7yg8ePHa+jQoTrnnHN0wAEHhHW9b775JrRtjNHAgQNrFNfuPvzww1ASp3fv3mrXrl1ErhsLr732mm688Ualp6drxIgRnrFnZGRUK9E1adKkaicfNmzYoDVr1ig9PV0pKSlKTEysMgm2q0KOVLqSYd26dZ5zrbWhfmAFBQXKz89Xdna2MjIyqhVnY9Qokg/Lly/X9u3bQ/t9+/atu2Ak9evXL9Rt/scff9SwYcMq/eYSCAT06quvhvZ9Pl+9KBsFAAAaHpuzTVq5xHXM9A3vh6/6wDfkJDkZTWVfeFgKejQFXrVMzn2j5bv2TmnHNjnPPSBt21z5hVPT5Rt+tUz/QyIfNKonax/pm08qHt+4TjZnm0yT5rGPCQCABuLjjz/WTTfdVK6Et9/v15gxYzR8+PBaX/9Pf/qT2rZtqxEjRmjbtj/KeS5YsEDXXHON7r33Xp199tk65ZRTlJ2d7XqNadOm6ZZbbtGcOXPKHT/wwAP1wgsvqEWLFrWK8fzzz1dWVpb+9re/6ffffw8dLyws1FtvvaW33npLHTt21JFHHqmRI0dqzz29e0pNmTIltB2piiqO4+i1114L7Z9wwgm1vmYsWGt133336fHHH5dU+uD+7rvvDvXaqK2aNFieNGlSaBVDTbzyyit65ZVXqnXOp59+qn33rb+rxuuLRtHz4ddffw1tp6enq2vXrnUYTenKi13fQEtKSvTss8961iUrLi7WI488Ui77NnDgwBpnfgEAQONm58zwHDN9G9bLDb4DDpXvmtul5Ep6YW1cL+ee6+Q8eFPViYdO3eS79RESD/WEyXJ/UCFJWrLAewwAgEZs+vTpOvvss3XFFVeUSzxkZmbqpZdeikjiYZfDDz9cn332mevqgQ0bNuixxx7T0UcfrREjRqikTD+uGTNm6IILLtDQoUMrJB5OP/10vfnmm5UmHnatZJCqflB90EEH6YsvvtCRRx7pOr569WpJ0h577FHpdX7++efQdr9+/SqdG65PPvkk9Ia/z+fTGWecEZHrRkPZVSlffPFFKPEgSf3799fo0aMjdq9Y9m5A9DWKlQ8zZ84MbWdnZ9eo9tzGjRs1cuTI0P4RRxyhK6+8skbx+P1+nXXWWXrmmWcklXa1HzNmjIYNG6a9995bKSkpKiws1Ny5czVhwgStXLkydG7Lli114YUX1ui+AAAAdo5HyaW27WXaeL/tVV+Z7H3lu/5uOY/dKeXucJ+0M7fq6ww+Ueasi2QSkyIbIGrMNGsptW4nbdpQYcwunS8zIDLlDgAAiBe//fabRo4cqVWrVpU7vtdee+nll19WVlZWldeo7lvnHTt21Hvvvad///vfeuCBB5Sfn19u/MQTT9Rjjz0WKsG0c+dO3XTTTRWSDgkJCbrpppt0+eWXV3nPsg+nd5V9qkzr1q312muv6YMPPtBdd92l9evXh8ZOOOEE3X333eWaaO9u+fLl5VZORCL5EAgE9MADD4T2hwwZok6dOtX6umWVLd9eW2X/vMp+jVx88cW69dZbK5TYCufPxUtNmjkPHDhQL730kvbcc0+1bNlSKSkpSklJqfTP9Y033tAtt9wiSbriiit0/fXXe8611qqkpESBQED5+fnKycnRXnvtVe04G6O4Tz4UFhZq2bJlof2ePXvWYTR/OPLIIzV79mz98MMPkqTFixfrnnvukSQlJiaWywjv0rp1a91www112iwbAAA0XDYQkObPdB0zfRrWqoeyTOfu8o2+T86jt0ubf6/6hLKSU2X+MlK+Aw6LTnCoFdM9W9Yt+UDfBwAAKujcubM+/fRTXXjhhZoxo3S16/HHH69HHnlETZo0Cesau791HgwG5ff7Kz3H5/Ppsssu02mnnaaHHnpIb775pgKBgDp06FAu8SCV1v9/7733dMkll+i7776TJHXq1Enjxo0Lu8R4UVFRaLvsKoiqnHbaaTr++OP1yiuv6JlnnlFOTo7uueeeSh9QS6WNr6vTyDgc48aNK/e8csSIERG9vlTaCHyXhISaPwIuLCzUe++9V+5YcnKyHn74YZ122mmu57g916zKhg0b9N1335VLnlX1Z7NL9+7d1b1792rdr+zXtd/vV0pKSqXzU1NLV1vXthxYYxP3yYeFCxeWy7ZV9wsxmq6++mq1atVKEydOLJc13P0vqN/v1+DBg3XeeefRyAQAANTc0vlSQb7rUEMrubQ703bP0gTEY3dKayo2KXbVYS/5Lh8t0659dINDzWXtI/34TcXjq5bLFubX+wbpAADEWvPmzfXKK6/oxBNP1KWXXqqLL764WufvnnwIBAJVJh92adOmje677z5dc801euWVV9S1a1elpVX8tzotLU3PPfecTjvtNB144IG68cYblZmZGXaMO3b8sdq1bCIiHCkpKbr88ss1fPhwTZ8+vU7Kmk+fPr1c2aLDDjtMBx98cI2v9+OPP+qjjz4KNVkuLi7WmjVrNGnSpNCc5s1r3ivrt99+04YNf7wM0qxZM73wwguVxlxYWBj29R944AE9/fTTrn+WrVu39jzv119/1caNG+X3+5WcnKzMzEylp6eHnbDYvPmPkqzbt2/X0qVLw465qKhIhYWFKioqUlFRkVJTU2v1ZxjP4j75sGDBH/VgjTHq1q1bHUZTns/n0/nnn6/Bgwfr888/15w5c7R582b5/X5lZmaqQ4cO6tOnjw4++GC1atWqrsMFAAANnJ0zzX0gJVXK6hXbYKLANGsh3/X3yHnybmnx3MrnHnaszJ8vlUlKjlF0qAnTvZes24B1pOWLpF77xTokAADqvRYtWujbb7+t8k1uN7uXTQoEAkpOrt7npT333LPK5r8ZGRn6/PPPa/RG/vbt20Pb1XnIXVZycrIGDox9Ccc1a9bokksuCSV5/H6/br/99lpds1evXrrggguUk5PjOWe//Wr+malnz5569tlnNXz4cDVp0kRvvfWWevfuXek5BQUFYV//r3/9q55//vkKyYeOHTtqwIABnuc999xz+uijj8K+T2XGjx+v8ePH1/j8/fbbTxMnToxILPGm3icfnnzyyVqdf8455+icc86pdRxt2rTRhAkTan0dNx06dNAll1wSlWsDAADsYmd79Hvo1U8mIdF9rIExaenyXXuHnH8/JM34seKEpGSZ80fId8iQ2AeH6mvXXspoIu2s+MO0XTJfhuQDAACuapJ4kKRNmzaV28/Pz1d6enokQqqgpqWAtm3bFtoOBAIqLi5WUlL979u1detWDRs2TBs3bgwdu+KKK5SdnV2r6zZt2lRnnXWW/v3vf7uOt2jRQpdddlmt7nH00Ufryiuv1PHHH19l4kEqTQrtWvXitvqlrFatWumYY47RBx98IGOM9t57bw0ZMkQXXXRRpX+ufr9fGRkZSk9PV0pKivx+f4XeE9Gya+XDrtUP4a4OaoyMtdb1ZSIgXBs2bKh2QyI0DgkJCeWWMG7cuLFa9RgBAJFjN66Xc7N7Az8z/Gr5Bh0d44iiyzpB2Qkvyn49Udr1cbd9Z/kuu15mz8g280N0BZ+8R5r5U8WBnn3k/8fdEbsPn1sA1HfWWm3ZskWS1LJly7BLiwDVcffdd+upp54K7f/www/q3LlzHUZU0Y4dO8q9Jd+6desG8/dh6dKleuWVV/Tyyy+rf//+euedd2r8wLzseZMmTdL5558vqTQZ0bFjR/Xo0UMDBgzQqaeeWu/7FCxYsEAbNmzQfvvt1+h73Ub6e73P51O7du0iEVqNkHxArZF8gBd+iAeA+sP56mPZN593HfM9+IpM05rXga3P7JoVsiuXyjRtIe2zn4zPV9choZqcSR/Ivv1ixYGkJPkeeyNiq3b43AKgviP5AMSPadOmqVu3brXqxVA2+ZCfn6/ff/9dLVu2DDVGRsMUb8mHel92CQAAALVnZ3v0e+jcPW4TD5JkOnSR6dClrsNALZgsj74PxcXSquVS156xDgkAAKBW9t9//4heLzExUR06dIjoNYFI4NUvAACAOGcLC6TFc1zHTN/I/uADRFzHrpJHY3C7ZH6MgwEAAAAQLpIPAAAA8W7hLMmjfIzpc0CMgwGqxyQkSN32dh2zS0k+AAAAAPUVyQcAAIA451lyqUkzqXO3mMYC1ITpnu0+sHS+LL3HAAAAgHqJ5AMAAEAcs9bKznFPPpg+A2jAjAbBZO3jPrAzV/p9bWyDAQAAABAWftoEAACIZ6uXS9u3ug5RcgkNRpcekkeizC6ZF+NgAAAAAISD5AMAAEAc8yy55E+QevWLaSxATZmUVKmTR4mwJQtiGwwAAACAsJB8AAAAiGNeJZeU1UsmNS22wQC1YLr3cj3OygcAjZG1tq5DAABEQbx9fyf5AAAAEKds7g5pxWLXMdNn/xhHA9SOyXJPPmjLRtmtm2MbDADUAWOMjDGSJMdx6jgaAEA07Pr+XvZ7fkNG8gEAACBO2bkzJI83Z0xfkg9oYLpnew7ZpfNjGAgA1B2/3y9JKikpqeNIAADRsOv7+67v9w0dyQcAAIB4NXuq+/HW7aS27WMbC1BLpkkzqZ3H1y3JBwCNRHJysiSpqKiojiMBAETDru/vu77fN3QkHwAAAOKQDQRk5/3qOmb6HhAXS3jR+Hj3fSD5AKBxSEpKkiQFAgEVFxfXcTQAgEgqLi5WIBCQ9Mf3+4aO5AMAAEA8WrZQKshzHaLkEhosr74Pa3+Tzd8Z21gAoA74/f7QA6mcnBwSEAAQJ4qLi5WTkyOpNPEQL2WXEuo6AAAAAESeneNRcik5RcrqHdtggAgx3XvJtYuJtaUJNxqpA2gEMjMzlZubG3pQlZCQoOTkZCUmJsrn87G6EWgkyjaetx593lB/WWvlOI5KSkpUVFRUbsVDZmZmHUcXOSQfAAAA4pCdPc19ILufTGJibIMBIqV1O6lpC2nH1gpDdsk8GZIPABoBY0y5BEQgEAg9tALQeJRNNJJ8iA+7Eg/xlEQm+QAAABBn7ObfpfWrXccouYSGzBgj0z1bdvqUCmN2yYI6iAgA6oYxRk2aNFEwGFRxcbGKiooUDAZ5AAk0IgkJfzzWLSkpqcNIUFPGGPn9fiUnJ8dVqaWySD4AAADEGTvHY9WDJNNnQAwjAaIgax/JJfmglYtlS4plEuOjOR8AhMPv9ys1NVWpqamSePsZaCz8fr/atGkT2t+4caOCwWAdRoSaiKcVDl5IPgAAAMQZO9uj30OnrjLNWsY2GCDCTFa2e9+HQEBaudS7KTUANAKN4UEWAMnn88nn85XbL9sDAqgvfFVPAQAAQENhiwqlhXNcx0zfA2IcDRAFHfaSUlJdh+ySebGNBQAAAIAnkg8AAADxZOFsKeBe85VmvIgHxueXuu3tOmaX0vcBAAAAqC9IPgAAAMQRO9uj30NmU2mvrNgGA0SJydrHfWDpAlmHescAAABAfUDyAQAAIE5Yaz2bTZve/WV8fPRDfDDdPfo6FORJ61bFNhgAAAAArvgJFAAAIF6sXSlt2+w+1od+D4gjXbIkf4LrkF0yP8bBAAAAAHBD8gEAACBOeJZc8vlk9ukX01iAaDJJydJe3d0HST4AAAAA9QLJBwAAgDhhZ091H+jeSyYtI7bBAFHmVXrJLpkva22MowEAAACwO5IPAAAAccDuzJGWL3YdM30puYT449l0evsWacvG2AYDAAAAoAKSDwAAAHHAzp0hWcd1zPTdP8bRADHQfW/PIfo+AAAAAHWP5AMAAEA8mOPR76FVW6ldh9jGAsSASc+U2nd2H1xK8gEAAACoayQfAAAAGjgbDJaufHBh+uwvY0yMIwJiw3TPdj3OygcAAACg7pF8AAAAaOiWL5Lyd7oOUXIJcc2r78P61bK5ObGNBQAAAEA5JB8AAAAaODtnqvtAUrLUs09sgwFiyHTv5T24jNUPAAAAQF0i+QAAANDA2dke/R6y95VJTIptMEAMmZatpRatXccovQQAAADULZIPAAAADZjdskla+5vrGCWX0Bh4rX4g+QAAAADULZIPAAAADZhnySVJpjfJBzQCWR6ll1Ytky0qjG0sAAAAAEJIPgAAADRgniWXOnSRadEqtsEAdcB4JR+CQWnF4tgGAwAAACCE5AMAAEADZYuLpEWzXccouYRGY4+OUlqG6xCllwAAAIC6Q/IBAACgoVo0Ryoudh0yfUg+oHEwPp/UPdt1zC4l+QAAAADUFZIPAAAADZSd7dHvISNT6tojtsEAdciz9NKyRbLBYGyDAQAAACCJ5AMAAECDZK317Pdgeg+Q8fljHBFQd0x3j+RDUYG0ZkVsgwEAAAAgieQDAABAw7RulbR1k/sYJZfQ2HTuLiUmuQ7ZJfNiHAwAAAAAieQDAABAg+S16kE+n8w+/WMbDFDHTGKi1CXLdcwuWRDjaAAAAABIJB8AAAAaJDvHo99Dt71l0jNiGwxQD5ju+7gPLJkna21sgwEAAABA8gEAAKChsXk7pWULXcdMnwNiHA1QP5isbPeB3B3SxvWxDQYAAAAAyQcAAICGxs6bITmO65jpS78HNFLdsiXj/uMNfR8AAACA2CP5AAAA0NDM9ii51KK1tGen2MYC1BMmNU3q0Nl9cOn82AYDAAAAgOQDAABAQ2KdYOnKBxem7wEyxsQ4IqD+MFnufR/sEpIPAAAAQKyRfAAAAGhIli+Wdua6DlFyCY2dyerlPrBxveyObbENBgAAAGjkSD4AAAA0IHbONPeBpCSpZ5/YBgPUN909kg8SpZcAAACAGCP5AAAA0IDY2R7Jh559ZZKSYxsMUM+YZi2k1u1cxyi9BAAAAMQWyQcAAIAGwm7dLK1Z4TpGySWglPFY/UDyAQAAAIgtkg8AAAANhJ3rsepBkulzQAwjAeoxr74Pq1fIFuTHNhYAAACgESP5AAAA0EB4llxq31mmZevYBgPUU55Np60jLV8U22AAAACARozkAwAAQANgS4qlBbNcxyi5BJTRtr2U2dR1yC6ZF+NgAAAAgMaL5AMAAEBDsGiOVFzkOkTJJeAPxhipe7brmF26IMbRAAAAAI0XyQcAAIAGwLPkUlqG1LVnbIMB6jmTtY/7wPJFsoGS2AYDAAAANFIkHwAAAOo5a63sHPfkg+ndX8bvj3FEQP1munv0fSgpln5bFttgAAAAgEaK5AMAAEB9t2GNtPl397E+9HsAKujUVUpOcR2yS+fHOBgAAACgcSL5AABAI2ULC2RXLZfdsa2uQ0EVPEsuGZ9M7/6xDQZoAIzf71mOzC4h+QAAAADEQkJdBwAAAGLPzvhBzmtPSjtzSx9g9z9E5qyLZVq0quvQ4MLOnuo+0K2nTEaT2AYDNBCmey/ZBbMqDixdIOs4Mj7ewwIAAACiiU/cAAA0MnbTBjkvPFKaeJAk68hOnyLntivl/PcjWSdYtwGiHJu/U/IoE2MouQR4MlkefR/ycktLmQEAAACIKpIPAAA0MvbriVJxUcWBogLZt/4t5+5/yP62NPaBwZWdN1NyHNcx05fkA+Cpa0/JY3UDpZcAAACA6CP5AABAI2KLi2R/+LrySauWybn7H3LefF62ID82gcHbHI+SS81bSe33imkoQENiklOkTt3cB2k6DQAAAEQdyQcAABoRO22KlL8zjImO7Fcfy7lthOyMH2StjX5wqMA6juzcGa5jps/+MsbEOCKgYfEqvcTKBwAAACD6SD4AANCI2MmfV++E7VvlPP0vOU/cJbtlY3SCgreVS6TcHa5DlFwCqma6e/R92LJRduum2AYDAAAANDIkHwAAaCTs2t+kZQtrdvLsqaUNqb94TzYQiGxg8GTnTHMfSEiU9u4b22CAhsir6bRY/QAAAABEG8kHAAAaCftdJasesvet+gLFRbLvvCznrr/L1jSJgWqxsz36Pezdt7SePYBKmcymUrv27oNLF8Q2GAAAAKCRIfkAAEAjYIsKZX/6xn2wey/5R42V78qbpBatqr7Y2t/k3DdazutPyYbTPwI1YrdvkVYtdx2j5BIQPpO1j+txu2RejCMBAAAAGheSDwAANAJ26v+kgnzXMXPE8aX/73ewfHc+KXPMUMlU8RHBWtnvPpdz6wg5P39HQ+oosHOme46ZPiQfgLB59X1Yt0o2jwQqAAAAEC0kHwAAaATs5C/cBzIyZQYMDO2alFT5zrpYvlsekvbKqvrCOdtl//2QnEfvkN24PkLRQpLsbI9+D3t0lGnVNrbBAA2Y8er7YK20jNJLAAAAQLSQfAAAIM7ZVcukFYtdx8whR8okJlU83qmbfDfeL3Pu5VJqWtU3mf+rnDuukvPJBNlASW1DbvRsSYm0YKbrGCWXgGpq1VZq1sJ1iKbTAAAAQPSQfAAAIM7Z7zxWPUgyhx/nPebzyzfkJPnGPCmz/6FV36ikWPaD1+WMuVZ28dyahIpdlsyVigpdh0yfA2IcDNCwGWNkPEov2aUkHwAAAIBoIfkAAEAcs4X5sj9/5z7Ys49Muw5VXsM0aynf5TfId/XtUss2Vd90/Wo5D9wk5+XHZXfmVDNiSJWUXEpNl7rtHdtggHjgVXpp5RLZkuLYxgIAAAA0EiQfAACIY/bnyVJRgevYrkbT4TJ9BpQ2pD7hTMnvr/reU/5b2pD6h69oSF0N1lrZ2VNdx0zv/jIJCTGOCGj4vFY+KBCQViyJbTAAAABAI0HyAQCAOGWtlZ38uftgZlOZ/Q6u9jVNcrJ8Z/xFvlsfDe8N/J05si89JuehW2Q3rKn2/Rql39dKmza4j/Wh3wNQIx06e/avsUvmxTgYAAAAoHEg+QAAQLxauVRatdx1yAw6WiYhscaXNu07y3fDv2QuuFJKS6/6hEVz5Nx5tZwP/0OJkyp4llwyRqZ3/9gGA8QJ4/N7Jkzt0gUxjgYAAABoHEg+AAAQpzxXPUgyhx1b6+sbn0++w4+Tb+zTMgcPrvqEQEB24pty7rhadsGsWt8/Xtk5HsmHLj1kMpvGNhggjniWXlq2QNYJxjYYAAAAoBEg+QAAQByy+Xmyv0x2H+zVT6bNHhG7l2nSTL6LR8n39zFSONfduE7Ow7fKeeFh2ZztEYsjHtiCfMmjBIyh5BJQK8ar6XRBvrR2VWyDAQAAABoBkg8AAMQh+/O3UnGR65ivmo2mw2V69ZPvjnEyJ/9ZCqMpsv3p29KG1JO/kHWcqMTU4MyfKQXd38A2fUk+ALXSpYfn9yb6PgAAAACRR/IBAIA4Y62V/c6j5FLT5lLfA6N2b5OYJN/Qc+W77XGpZ5+qT8jfKfvak3IeuFF27W9Ri6uhsLOnug80ayF17BrbYIA4YxKTpM7d3Qfp+wAAAABEHMkHAADizbKFkseDfDPoGJkwViXUltmjg3zX3SXz12uljMyqT1i6QM7Ya+W894pskfuKjXhnHcez34Pps7+MMTGOCIg/Jmsf1+N2yTxZa2McDQAAABDfov/0oZZ27typG264QZs3b9aIESM0ePDgsM994oknNHmyR71rDzfddJP69etXvSAjZPny5Zo0aZLmz5+vLVu2KCkpSR07dtQRRxyhIUOGyOcjVwQAqJpno2ljZA6vfaPpcBljZAYeKdt3f9l3Xpad8t/KTwgGZT97V3bq9/Kde4VMnwGxCbS++G2ZlLvDdYiSS0BkmO69ZPVuxYHtW6XNv0tt28Y+KAAAACBO1fvkw1NPPaXNmzfX6NwVK1ZEOJrocBxHr7/+uj755JNyb1yVlJRo4cKFWrhwob7++muNHj1aTZo0qcNIAQD1nc3LlZ02xX2w9wCZlm1iG5Akk9FEZvjVsgOPlPP609L61ZWfsPl3OY/fKbP/oTJnXyLTrEVsAq1jdo5HyaWEBGnvfWMbDBCvumd7DjmL50n79I1hMAAAAEB8q9ev0o8fP17TprmXH6hKUVGR1q5dG+GIouPll1/WxIkTK13qvWTJEt15550qLCyMYWQAgIbG/vi1VFLsOuY7/LgYR1Oe6dFbvtselTntfCkxqcr5dtr3cm4bIeebT2Qd9ybM8cTO9vjM06OPTEpqbIMB4pRJz5Dad3Ydc2g6DQAAAERUvV35MHHiRH344Yc1Pn/lypVyHEeS1LFjR917771hnZcQgzrYZU2cOFGff/5HeYzk5GSdeeaZGjhwoFJSUjRnzhy9+uqr2rZtm1avXq1///vfGjlyZExjBAA0DKWNpr9wH2zWUupT96V7TEKizElnyR5wmJzxz0jzf638hIJ82f88K/vjN/KdP0KmU3w2XbY7tkm/LXUdo+QSEFkmq5drg3u7mOQDAAAAEEn1cuXDO++8o1dffbVW11i+fHlou2PHjkpKSgrrv1j2Vdi+fbveeuut0H5KSoruvPNOnXbaaWrTpo2aNGmiQYMG6c4771Rqaukbj5MnT9aSJUtiFiMAoAFZMk/asMZ1yBx2rIzfH+OAvJk2e8h37R0yl1wnNWlW9QkrFsu5e5ScCS/IFhZEPb5Ys3One46ZepA0AuJK916uh+361Qru2BbjYAAAAID4Va+SDyUlJXr22Wc1YcIESaWNKmu6EqFs8qFTp04RiS/S3n33XRUVFYX2L7roInXtWvGNznbt2un0008P7b/33nsxiQ8A0LB4rnowPplDj4ltMGEwxsh30BHyjX1K5ojjqz7BcWS//FDO7VfKzvwp+gHGkJ3t0e+hXQeZNnvENhggzpks9+SDJBXNnxXDSNBQ2aIiOd9/Keflx+V8/KZs7o66DgkAAKBeqjfJhx07dujWW2/VV199JUny+/266qqr1Lx58xpdr2zyoUuXLhGJMZKKior07bffhvY7duyoI444wnP+UUcdpcTEREnSzJkzlZOTE+0QAQANiM3NkZ3h0Wi67/4yLVrFNqBqMGkZ8p0/Qr5/3u9Zi72crZvlPHmPgk/eLbt1U/QDjDIbKJHmz3Qdo+QSEHmmRWupRWvXsaJ5VZSCQ6NmnWBp0uGWy2VfGSc75b+yH/1Hzp1Xx8W/RwAAAJFWb5IPa9asCSUMMjIydPPNN+vQQw+t0bUKCwvLNZuuj8mHOXPmlFv1MGTIEBljPOdnZmbqoIMOkiQFg0HNnTs36jECABoO+8NXUiDgOuYLZ1VBPWC67S3fLY/IDBsuJSVXfcLMn+XcOqK0FNP2rVGPL2qWzJc8SklRcgmIDq/VD8XzZsY2EDQI1lrZ2VPl3HmN7CvjpN3/zdmxTfaTCXUTHAAAQD1Wb5IPu/Ts2VP333+/evfuXeNrlG023bp1azVr1ixC0UXOsmXLyu3379+/ynN69OgR2l60aFHEYwIANEzWcWQne5RcatlG2me/2AZUCyYhQb7jzpBvzJPhNcguLiotxXTjpXL+80yDfPPUzp7mPpCa5lmbHkAtZe3jerh42UI5cdhXBjVnVyyR8+DNcsaNldat8p7360+y//8zKAAAAErVrKFCFCQlJem8887TKaecUuumz2Uf7Jd9YF+frFu3LrTdtGlT7bnnnlWes9dee4W216xxbygKAGiEFs2RNq5zHTKHHSvjqz+NpsNlWraR76pbpRk/ynnzuYpvme4uUCL7zaeykyfJDDxS5oRhMq3bxSbYWrJzPJIPvfrJ1LD3FYDKme69ZN0GgkEVL5qrlH0PiHVIqGfsxvWyH7wuO/V/4Z2Qu0Nas0Lq1C26gQEAADQg9eYn2qysLGVlZUXkWmX7Peyxxx6aNGmSpk+frvXr12vr1q1KTk5Wq1at1KdPHw0aNKhOyjJt3frHQ5SOHTuGdU7btm1D2xs2bIh4TACAhsl+97n7gN8vM+jo2AYTQcYYacBA+Xr1k/1wvOzXn0i2irdKgwHZ/02SnfJfmYMGy5z4J5l27WMTcA3Yjeuk39e6jpk+PPwEomaPDlJ6ppSXW2GoaN6vJB8aMZu7Q/aTCbLffiYF3csZep47f6YMyQcAAICQepN8iKSyKx/eeeedCuPFxcXKzc3VihUr9NFHH6l///66/PLLa9zcuiby8vJC2+3ahfdmZnp6emh727ZtEY8JANDw2JxtsjN/ch/c90CZZi1iG1AUmNQ0mT9fKnvwYDmvPSWtWlb1SY4j++PXsj99K3PAoTInniXTvlP0g60mz5JLkkyfqksyAqgZ4/NJ3bOlWb9UGCui70OjZIuKZP/7oezn73r24anyGvNnSsefGdnAAAAAGrC4Sz7k5+dr/fr1FY4bY9SiRQs1adJEmzdvVm7uH285zZgxQ7feeqvuuuuumPWHKCkpCW2Hm/RISkpSYmKiSkpKFAgElJubq8zMzFrFsWPHDuXk5IQ93+fzqX378m+Q+v3+WpfKQnzy+/2V7gOovcAPX0vBoOtY4pEnyRdPZXu6Z8ve9qicbz5V4OM3pZwwEvHWkf1lsuwvk+Xbf5D8p/xZvnr0VmqxR8kl07WHElu0jnE0QCPTs7eCLsmH4oVzZIMBPrc0EtYJyvn+KwXff03avqV2F1syT/5gQCY5JTLBAQDggectCJcxpk7vH0dPJEotXbpU1v5RwbVFixY666yzNHDgQKWk/PEhcPny5Ro/frzmzJkjSdq4caNefvllXXvttTGJs2yM1UkgJCcnhxIXeXl5tU4+fPHFF66rQ7xkZmbqhRdeKHesdWsejiA8LVu2rOsQgLhiHUfrv/+v65i/XXu1PeLY0rd74825F8s58zzlffGBct95VcEtG8M6zZk2Rc60KUo58DA1+fPFSu7ZO8qBVhFPfp7WLp7rOpZ5yBA1bdMmxhEBjUvRgYdq44QXKxy3hQUqWb5YLbNo+B7PrLUqnDpF219+XIHflld9wv9L3vcApR1xnLY9flfFwUBATTauVeqAQyIYKQAAVeN5C+qruHsisXjx4tB2586ddd999+nII48sl3iQpK5du+rmm2/WwQcfHDr2448/avXq1TGJs+xKgaSkpLDPK5vJLCio2XJgAEB8KPz1ZwU9+gVkHH96fCYe/p8vOUWZp/5Ze7zwgZqPvFH+tnuGfW7hL//TxlHDtenWkXVaXqVw5i9SwL2eeOoBh8Y4GqDxSeqeLZOU7DpWNPfXGEeDWCpaPE+bbrxcm++8NuzEQ+Je3dXqzsfV+u6nlH7MqfI1aeo6r/BXj1KIAAAAjVDcrXw45ZRTdPDBB2vTpk3q3LmzmjZ1/1AolSYARo4cqWXLlmnTpk2y1uqbb77RhRdeGPU4ExMTa3ReQpnyGWVLNwEAGp+8z95zH0hIUPoxp8Y2mDpiEpOUccKZSj9mqPK++VS5E15SYF14LxIUzvhJhTN+UnKfAWpyziVK7rt/TJekFk793vW4r3lLJXbrGbM4gMbKJCYqqec+Kpozo8JY0fyZyjz9vDqICtEUWL9G2199UgWTvwz7HH+rtmp6/hVKO/JEmV0vghmj5H4Hul6naAbJBwAAgF3iLvmQnJysDh06qEOHDmHNT0pK0rHHHqvx48dLkqZOnRqT5EPZlRgBj7ce3ZSd6zhOreM47rjjdMgh4S8LduvtsCtxA+zO7/eXW/q3ZcsWBT1q0wOoHrtti4p//s51zNd/oLYUB6SN4ZUjihv7Hixf7wOU8MtkBSe+KRtmEqJoznRtmjNdpnu2Ek49R6b3gKgnIay1Kv7lf+6DvQdo0+bNUb0/gFKBvXpIbsmHeTO1efPmiHzeRt2zOTsU/PgNBb/5VAqG+bNXarr8J50l/zGnKi8pWXlbyveDCHbbR3JJPpT8tky/L14o06xFJEIHAMAVz1sQLmNMnZbMj7vkQ01kZ2eHtn///Xfl5eUpPT09qvfMyMgIbVen4fPOnTsjGkfTpk0rXR0SjmAwyA9mCEswGKxWsg2AN+e7zySv772HHdu4/64dcJjMgEEyv/4oZ+Jb0pqVYZ1mly5QycO3SZ27y3fy2dK+B0YtCWF/WyZt3+o+2Lt/4/7zA2LIdt3b9bizY5sC61bJadUuxhEhkmxRkex/P5T9/F2pMMyStf4EmSEnyZz0JymjiYKSa4k8u3cfz0uUzJ4m38AjaxY0AAA1wPMWeHF7kTyWSD6oYsPnLVu2RD35UPaB/9atHg8fdlNQUFAui5mc7F6jFgAQ36wTlP3fJPfBtu2lnt4PRBoL4/NJAwbJt98h0uyppUmI35aGd/JvS+U8ebfUoYt8J58l7XdIxPtn2NlT3Qf8CVKvfhG9F4BKdNtbMj7JVkzm2sXzJJIPDZJ1grI/fC374XjvRK8Lc+ARMqedJ9O66j9306K11K6DtGFNxcH5v0okHwAAAEg+SFJeXl65/Vg0cm7X7o8PtOE2ud68WwmGtLS0iMYEAGgg5syQtrqX5TGHHxfTvgX1nfH5pH4HybfvgdK8GaVJiGULwzt5zQo5z9wn7dFR5sQ/yRxw2B/1vmvJzpnmPtBjH5kU/n0HYsWkpkkd95JWVWw67CyZJzPwqNgHhRqz1kpzpsl59xVp3arwT9y7r3zDhst07l6t+5l99pN1ST7YBbNkHSfiiWsAAICGJm6TDyUlJWE3dd595UFqamo0QiqnS5cuoe0VK1aouLhYSUlJlZ6zaNGi0LYxplxtNwBA4+FM/tx9ICFRhjctXRljpN4D5Nunv7RwtpxPJkiL5oR38vrVsi88LPvxG6VJiIMGyyTU/COUzdkurVziHmff/Wt8XQA1Y7L2kXVLPiyep8ikGxELdsUSOe+8JC2eG/5J7TvLN2y4tE//GiXuTa9+sl99XHEgZ7u09jepY5eKYwAAAI1IXCUf1q1bp88//1yzZs1S586dNWrUqLDOmzv3jw+osXqon52dLb/fr2AwqOLiYs2fP1/9+vWr9JyFC/94U7N169ZKqMWDj8bK5udJeblSq7a8GQygQbJbN0lzpruOmQEDZTKaxDiihsUYI2XvK3/2vrJL5peuhJj/a3gnb1wv+/Ljsh+/KXPCMJmBR8mE+aJDWXbudMla9/j6HFDt6wGoHdM92/0B8sb1stu30ji4nrMb18t+8Lrs1P+Ff1LzVjJDz5M5ZLCMrxYpph69S8vluTSxtvN/lSH5AAAAGrm4enq9c+dOff556dugW7duVWFhoVJSUio9p6SkRFOn/lF3ea+99op6vwdJSklJUY8ePbRgwQJJ0tdff11p8iEQCGjWrFmh/R49ekQ7xLhiCwtkxz8t+9O3pQf26CjfiBtl2nWo07gAoLrs/750rU0uSeaIE2IcTcNmsnrJ//c7ZZcvKl0J4dWHYXdbNsq+/pTsJxNkjjtD5rBjZJKq0YdptkfJpTZ7yrTdM/zrAIiM7r28x1YukfodFLtYEDabu0P2kwmy337m+vDfVWp6afL4qJOr933bg0lJLe0b4rLaws6fKR13Rq3vAQAA0JDFVRHK7t27h5pHFxUV6ZNPPqnynA8//FBbtmwJ7R944IFRi293gwcPDm3/8ssvWrlypefc77//Xjt27AjtV7VKAn+wwaCcZ/71R+JBktavlvPQLbJ5O+ssLgCoLhsMyn7v0Wh6j45S9+zYBhQnTNee8l91q3y3PiL1PyT8E7dtln3zOTk3Xipn0vuyRYVVnmIDAVmPlRaUXALqhmnWQmrZxnXMrv0txtGgKraoSM4nE+TcdFnpipVwEg/+BJmjh8p3z7PynXBmRBIPu5he/dwHFs+TLS6K2H0AAAAaorhKPvh8Pg0ZMiS0/8EHH2j58or1W3f59ttv9e6774b2U1JSdNxxx0U1xrIGDRoUWmXhOI6eeOKJCs2vJWnjxo16/fXXy8UZyyRJQ2cnvCDNc3nQs32r7DsvxT4gAKip2VOl7Vtdh8wRx1NOrpZMp27y/+1G+e4YJ3PAYVK4v58522XffknOPy+R8+nbsgX53nOXLZA8xk0fkg9AnWnf2f34utWxjQOerBOU8/2Xcm65XPaD16XCgrDOMwceId/Yp+Q7++KolCY0vfZzHwiUSEvmR/x+AAAADUlcJR8k6ZRTTim3+mHMmDH69NNPtW3bNkmlD/l/++03Pffcc3rqqacUDAZD515yySXKyMjwvPZZZ50V+u+OO+6odaxJSUk65ZRTQvurVq3SbbfdFurt4DiOZsyYodtvv105OTmheSeddFKV5aRQyvnmE9mvJ3qO2++/lF0wy3McAOoTz0bTiUkyBw9xH0O1mfad5bvsevnGPClzyBDJF+bHpZ05su+/JuefF8v56D+uq+usV2mn5FSpxz61iBpAbZj2nVyP23WsfKhr1lrZ2VPl3HmN7CvjPJPwFezdV75bHpbv0utkWreLXoCdu0rpma5Ddv7M6N0XAACgAYirng+S1LRpU/3tb3/TQw89pGAwqPz8fL388st6+eWX5ff75TiO7G5NHn0+n8477zwdfvjhMY/31FNP1axZs0K9H1avXq3bbrtNmZmZofjLat++vYYOHRrzOBsiO3eG7JvPVznPee1J+e4YF9Hl1wAQaXbTBvdVXJLMAYfJpHsnz1Ezpl0HmYv+LnvKObKfvSP7w9fhlffIz5P9+E3ZLz+UGXKSzDGnyWSWvm1rvfo97NNPJqH6zasBRMieHisfNqyRDQRkEuLux6YGwa5YIuedl1x7Knhq31m+YcOlffrHZEWg8fllsveVnfZ9hbHSMnt/jXoMAAAA9VXcrXyQpP3331///Oc/1bx583LHg8FghcRD9+7ddccdd5RbgRBLCQkJGj16tPr27VvueG5uboXEQ+vWrTV69GhWPYTBrlsl57n7Jce9KWs5mzbIfvxm9IMCgFqw/5sk7fZv2C7m8NiVDGyMTOt28l04Ur67n5UZcqIUbpKgsED2s3dKV0K8/aLs0gXShjXu96DkElCnjFfZpUBA2rQ+tsFAduN6Oc89IOee68JPPDRvJTP8Gvlue1Sm94DYliL06vuwZqXsjm2xiwMAAKCeidtXePbdd1899thj+uqrr/TTTz9p1apVKikpUWZmplq0aKHs7Gztv//+6tWrV12HqrS0NN100036/PPP9c4772jnzvJlGowxGjRokIYPH64mTSJfpzTe2NwdcsaN9ayp7XrOpPdlDzhUplO3KEYGADVjAyWyU/7rPthhL6lrz5jG01iZlq1lzr1C9sQ/yX7xgezkz6Ti4qpPLC6SnfSB7KQPvK9N8gGoW+3al5ZYc3txZe1v0h4dYx9TI2Rzd8h+MkH228/CW2kmSanpMicMkznq5DpbyWx69ZP76wGSXTCT0ogAAKDRMnb3pQCoU8XFxZo2bZpWrFihkpIStWnTRv3791e7dlGsU1pLGzZskBPOCoMYsCUlch6+VVpag+ZunbrJd9ODMn5/5ANrpBISEtSmTZvQ/saNGxUIhPmDJIAQO+17Oc/e7zpmzrtCvsEnxjgiSJLN2S775Yey33wqFYXX+NRV5+7y3/Jw5AIDUCPObVfKrq/YYNqc/Gf5hp5bBxE1HraoSPa/H8p+/m7YjaTlTygtbXfSn6LSSLq6grf+TdqwtsJxc/AQ+S7+ex1EBACIZzxvQbh8Pl+dPleO25UPDVVSUpIGDhyogQMH1nUoDY61Vva1JypNPJgjjpf99ScpZ3vFwVXLZP/7ocxxZ0QvSACoAWfyF+4DySkyBw2OaSz4g2nSTObMv8ged7rsVx/LfjVRKsir/nVY9QDUC6Z9Z9fkA02no8c6Qdkfvpb9cHz4jaQlmQOPkDntvOg2kq4mk91P1iX5YBfMlLU2tmWgAAAA6om47PmAxsl+9o7sj994T9j3QJlzL5fv3Mu9r/Hhf2Q3rotCdABQM3bjOmnBLNcxc+DhMqlpMY4IuzMZTeQbep58/3peZuh5Unpm9c7vS/IBqA88+z6sXRXbQBoJm7dTzkO3yL4yLvzEw9595bvlYfkuva5eJR4kyeyzn/vAjm2lpbsAAAAaIZIPiAt2xg+y77/mPaFDF/kuuU7G55f6D5T6Hew+r6RYzmtPVWhMDgB1xXqtehCNpusbk5Yh38lnlyYhzvyLlNm06pMym0qdu0c/OABVMh08kg8b18sWF8U2mEbAvv+qtHheeJPbd5bvmtvlGzVWpr5+z+zZW/Io32rn/xrjYAAAAOoHkg9o8OxvS+W8UEmt7CbN5Bt5i0xKqqTSBt6+cy+XvN4WXjhb9oevohApAFSPLSmRneLx/ahzd5m9smIbEMJiUtLkO/5M+e79t8zZF0tNW3jP3f9QGR8fx4D6wLTfy33AOtKGNTGNJd5Zx5H9ZXLVE5u3kvnrNfLd9qhM7wH1unSRSUmTuvZ0HbPzZsY2GAAAgHqCn3bRoNltW+Q8cZdUXOw+ITFJvitvlmnZutxh07ylzJnDva874UXZHdsiGCkAVJ+d8YO0M8d1jFUP9Z9JTpbv6KHy3fuczLlXSC1alZ/Qup3MyWfVTXAAKjBt9pASk1zHLKWXImvLRqkg33s8NV3mjL/Id9fT8g08qnT1cgNgenmUXloyT7bE4+cVAACAOEbDaTRYtqhQzhNjK60Ra/56rYzHG0jmsGNlf/5WWuLSoDp/p+ybz8tcfkOEogWA6vMsuZSSKnPg4bENBjVmEpNkhpwoe9gx0qxfZJcvklq0kTlkiExael2HB+D/Gb9fiR33UsnyxRUHqdkfWWtWeg6Zo4fKnPQnmYwmsYsnQsw++5U2z95dSXHpzxy9+sU8JgAAgLrEygc0SNZxSkstrVruOccMPVe+Aw71Hvf55LtwpJTgnoOz076XnflzrWMFgJqw61dLi+e6jpmDB4dKyaHhMAmJMgMGyfeni+Q76mQSD0A9lNi5m+txu46VD5FkvZIPrdvJd/bFDTLxIEnq3E1Ky3Adou8DAABojEg+oEGyH7wm/fqT57g58AiZk86u8jqmXQeZk//sOe6Mf1o2P69GMQJAbVTeaPr4GEYCAI2HV/JBJB8iyjP54NV3o4EwPr+U3dd1zM6fGdtgAAAA6gGSD2hwnClfyX72rveEbnvLDL8q7IZ05rjTpfad3Qe3b5V9/9UaRAkANWeLi2R/+Np9sEsPmY5dYhsQADQSnsmHLRtlK+tRgOrxKGNlOuwV2ziiwLPvw+oVsjn0lAMAAI0LyQc0KHbxXNnXnvSe0LKNfCNulPFoFujGJCTK95erJOP+18F++5msW18IABFj83bKmfCCgneNUvDpf8lWUlKtMbDTpkj5O13HzBGsegCAaPFMPkisfogQW1QkbVznOhYfyYd+nmN2/qzYBQIAAFAPkHxAg2E3rpfz9L1SMOA+ISVVvqtulWnSvNrXNl16yBx1iue48+oTsiXF1b4ugKrZQEDOw7fIfvmh9NtSacYPcu79h+yc6XUdWp2xkz93H0hNl9n/sNgGAwCNiL91O5nUNNcx+j5EyLpVkrXuY/GQfGjVVmqzp/sgpZcAAEAjQ/IBDYLN3yln3FhpZ677BOOT77LrZbzKJ4XBnHae1LKN++CGNbKfvl3jawOoxIKZFZvHBwJynr1fdvWKOgmpLtm1v0nLFrqOmUOGyCQnxzgiAGg8jM+nxE5d3Qc9SgWheuwaj3/bk5Kl1m1jG0yUmH36uR6382fKeiVeAAAA4hDJB9R7NhiU8+z90oY1nnPMWRfJ9Nm/VvcxySnynT/CO47P3vFujgegxuyKxe4DRQVyHh8ju3VzbAOqY/Y7j1UPotE0AMSCV+klVj5EiFcSp33n0obNccCz9NKOrZTvAgAAjQrJB9Rr1lrZN5+rdImyOeL4SksmVYfp3V/m4CHug8FgafklJxiRewH4f9u3VjK2Rc64sbKFjaPJpy0qlP3pG/fB7r1k2neKbUAA0Ah59n1g5UNEeL3MU5sVzPVOz76Sz6OfHKWXAABAI0LyAfWa/foT2W8/856Qva/Mny+TMSZi9zRnXSxlNHEfXLFY9utPInYvAJKtLPkgSWtWyHn2Adlg/Cf+7NT/SQXuiRZzxHExjgYAGifP5EPOdtncHbENJs5Ya6W1K90H46Dfwy4mNU3qurfrmJ3/a4yjAQAAqDskH1Bv2TnTZd/6t/eEdu3lu2K0TEJCRO9rMpvI/PlS77g+eF128+8RvSfQqG3fUvWcudNl33wu7usk28lfuA+kZ8oMGBTbYACgkfJMPkiUzKmtHVs9e7iZOEo+SJWUXlo8V7akOKaxAAAA1BWSD6iX7Nrf5Dx3v2Qd9wnpmfJddatMWkZU7m8OPFzqPcB9sKhQzutPxf1DUCBmqlr58P/st5/JfvlBdGOpQ3bVMsmj/4U55EiZxKQYRwQAjZOveUv5mjR1HbOUXqqdyvqnxVPZJVWSfCgulpYuiGksAAAAdYXkA+odm7NdzrixUmGB+wR/gnx/u1GmzZ5Ri8EYI9/5f5OSU9wnzPtV9ufvonZ/oLGwgYBUjRIW9p2XZaf/EMWI6o79zmPVgyi5BACxZIyppO8DKx9qw6vfg5q1lPEqe9pQ7ZUlpaW7DtH3AQAANBYkH1Cv2JJiOU/dI23Z6DnHXDBCpmfvqMdiWraROf0Cz3H71vPU/QVqK2db9eZbK+eFh2WXL4pOPHXEFuZ7JzR79pFp1yG2AQFAI+eVfLDrWPlQK14rRzrE16oHSTJ+v7R3X9cx+j4AAIDGguQD6g1rrewr46RlCz3nmOPOkG/Q0TGLyQw5Uera031wZ67shBdiFgsQlyorueT3ux8vKZbzxF2ymzZEJ6Y6YH+eLBW5r/Yyh7PqAQBirbKVD5TerDmvlQ+m/V4xjSNWTK/93AdWLeclJgAA0CiQfEC9YT+ZUHkpo34Hy5xxYewCkmR8fvkuvEryuze1tj99Kzt3ekxjAuKKV7PpxCSZ4Vd7n5e7Q87jY2TzdkYnrhiy1spO/tx9MKOJzH6HxDYgAIASO3d3HyjIk7Z5/NuFStlAibR+jftgnDWb3sWz74MovQQAABoHkg+oF+y072U/HO89oVNX+S4ZJeOL/Zesad9J5oRhnuPOa0/JevWnAFAp67XyoVkL+Q4eInPqud4nb1gj5+l7Sx9mNGQrl0qrlrsOmUFHyyQmxjggAEBip67eg+vo+1AjG9ZKwYDrkInX5EPrdlLrdu6DJB8AAEAjQPIBdc6uWCLnxUe9JzRtId+Vt8h4NX+OAXPinySvmutbN8l+8HpsAwLihVfyoWkLSZI5+WyZQ470Pn/RHNlXnmjQJTA8Vz1IMocfG8NIAAC7+DKbSM1auo7R96FmPJtN+xOkdu1jGkssmX3cSy/Z+TMb9OcXAACAcJB8QJ2yWzfJefIuqaTYfUJSknwjb5Zp0Sq2ge3GJCbK95eRnuP264lx1wAXiAmP5INp9v/JB2NkLrxS6tnH8xL2p29kP34zKuFFm83Pk/1lsvtg9r4ybfaMbUAAgBDT3qMJ8lpWPtSIV/KhXXuZhPhd5efZ92H7Fmn96tgGAwAAEGMkH1BnbGGBnHF3STu2ec7xXfR3mb2yYhiVN9O9l8zgE90HrZXz6hMNv/wLEGN2h1fZpT/eNjUJifL97Ubv1UeS7MdvyPnxm0iHF3X252+l4iLXMd8RJ8Q2GABAOb4O7skHu5aVDzXh9fsWryWXQnr2kTxKx9r5v8Y4GAAAgNgi+YA6YZ2gnH8/JK1Z4TnHnHa+zIBBMYyqauaMCz2X4Gvtb7KfvxfbgICGzqtp5/+vfNjFpGfId/VtUmZTz0vZV8bJLpoTyeiiylor+51HyaWmzaV9D4xtQACAcjxXPqxfJes4sQ0mHnitfIjz5INJS5e69HAds/NnxTgaAACA2CL5gDph33tVmvWL57g5eEhpn4V6xqSmyXf+3zzH7Sdvya5fE8OIgAbOc+VDiwqHTOt28o28RUpMcj8nGJDz1D0N5+/gsoWS11ugg46WSUiIcUAAgLJM+73cB4qLpc2/xzSWhs7uzCktM+Qi7lc+qJLSS4vmyJawchoAAMQvkg+IOed/k2S/eN97QvdsmQtHyhgTu6Cqwex7oMwBh7kPBgKl5Zd4Gw6oki0qkvLzXMeMS/JBkkzXnvJdMkry+v6Qnyfn8Ttlc7ZHKMro8Ww0bYzMYTSaBoC6Ztp38h6k6XT1VFaqqlEkH/q5DxQXScsWxDSW/2PvzsPrLsv8j3+ec7Lvbdq0TdIk3egGpWUr+yKryOqCCIKOG4o6jjrqjIqIOOO4Db9xcIdRRwRERBFGAQHZ99JC6UbXNE3aps12si/nPL8/gqEl3yc9Sc75nu39ui6uKznPWe62NE3O/b3vDwAAgJ9oPsBXdtNa2d/82H2HaTMUuPbLMtnJHTpnLv+IVFDkfbhlvftNRQBvck09SFKpd/NBksxRJ8q8+4Pux+7fq8gP/03WkaWQDGx3l+xLT3sfLj1KZtoMfwsCAIxicvOk6TM9zyyh0+NiXSuXCovH/Dc/bcw5TMov8Dyy69f4WwsAAICPaD7AN3ZvkyI//g8pHPa+Q36BAp+6TmaMne7JwpRMkbnsw85z+/tfybbu97EiIAW1j9F8cEw+/J05+xKZ08cIZN62SZFbb0raKST77KPS4IDnWeC0c32uBgDgVOmYfiB0enxcv1/VdUk77RxLJhiUFi3zPKP5AAAA0hnNB/jCdncp8t83St2d3ncwAQU+9oWxx9uTjDnxbdLiI70P+3oVuf0nstb6WxSQQqxj97PyC2Ty8sd8rDFG5vKPSUcc477Ty8/I3vOrSVQYH2MGTZeVS0cc629BAAAnV+i0bWLyYTxckw+ZkPfwd87VSzu3ynaGfK0FAADALzQfEHd2aEiRn/yHtLfReR9z+UdkDj/ax6omzxijwPuvlXIc4bevvCCtcqxVAeCefIhy/YIJBhX42D9Ls+c472Mf/IMij/1lItXFz+Z10h7vUGxzytnDV0cCAJKDa/Jhzy7ZIYKCo2EjYffkg6O5k46codPWym58xd9iAAAAfELzAXFlrZW946fSxled9zFnnK/A2y7wsarYMRWzZC660nkeuf2nsq5pDyDTuTIfDrFy6UAmr0CBT39teGLAwd7+U9m1q8ZbXdzYxx/0PjABmZMJmgaAZOKafFA4LO3d7W8xqWrf3uFgZQ+m2n0BQboxFbOcGSJat9rfYgAAAHxC8wFxZR/5k+wTjjfaJGnJCpn3ftS/guLAnHWRVDPP+7CzQ/Z3v/C3ICBVOCYfzDiaD5JkppQr8I9fk3Idq5psRJGffke2Yft4K4w52xmSfdkxEbXsGJmp0/wtCAAwtplVkmMizTaR+xAVV9i0Me7JkjRlFi/3vN2uX8O6VgAAkJZoPiBu7Csvyt71P+47zJqtwDVfTPkVIyYYVOADn5YC3n+d7NMPy25glBp4K+tauzTGFIOLmT1HgY9/0fn3UP29ivzgGwkPgrfPPCINDXmeBU4laBoAko3JypYqKr0PCZ2Oim3c4X0wfZZMbq6vtSSaWbrc+6Btv7THvaIWAAAgVdF8QFzYXdsV+fn3JNcVPEXFCnz6OpmCQn8LixNTM1fm3Eud55Ff/1C233vcHMhYrsDpcU4+/J05/GiZKz4+5utF/vtG2b6eCT3/ZNlIxD0JNnW6dPhR/hYEAIgKodOT4wqbVgaFTY9YtEwyjguW1rN6CQAApB+aD4g5G2pT5L+/KfX3et8hK0uBa78i49p5mqLMBZdLFbO8D/ftkb3vdn8LApKYtTZma5cOFDjtvDEbgdq1XZGfflc2HJ7wa0zYprVSc5PnkTnlHJlAak+BAUDacq0GaqT5EBVH88FkYPPBFBRJcxZ4nllyHwAAQBqi+YCYsgP9ivzw36XWfc77mKs+JbNgiY9V+cPk5Cpw1Sed5/ahe2Xrt/pYEZDEenuc4ZMqnXjzQZLMOz8gHX2i+w6vrZK982e+71a2jz/gfRAIyJx8lq+1AACiZ6oczYd9u2Vd/5ZBkmT7eqV9ezzPMrH5IElmyQrvg9dfkx0a9LcYAACAOKP5gJix1sr+8gfStk3O+5i3v1uBE9/mY1X+MouWyZxyjvehjSjyqx/IOva9Axmlw5H3IE147dLfmUBAgQ99Vpq70Hkf+9hfZP/6x0m9znjYUJvsmue8D5evlJlAzgUAwCeV3muXZK20e5e/taSasXIxMrX54Mp96O+Ttrp/jgIAAEhFNB8QM/a+O2VffNJ9h6NOkLnk/f4VlCDmXR+USqd4HzZsl334Xl/rAZKSK2xamvTkg/TGJNInvyJNm+G8j737l7Krnpn0a0XDPv2I5Fj1FDj1PF9qAABMUMVMKSvb88gSOj0m5+9Pbp5UXuFvMcmi7jApL9/ziNwHAACQbmg+ICYiLzwhe98d7jvUzlfgQ5+VCaT//3KmsEiB913jPLd/ukPWsfcdyBS2zRE2XVQik+39Bs94mZIyBf7xeqmgyFGEVeTW/5QdY1orFsYMmp4+U1p8ZFxfHwAwOSYQlCpnex820XwYkytsuqo2I34u8GKysoaDpz3Y9Wv8LQYAACDOMvM7PsSU3bFZ9hf/5b5DWbkCn/qKTG6ef0Ul2lEnSMuP9z4bHFDkf3/o+755IKm41i5NcuXSW5lZ1Qpc+2UpmOV9h8EBRW7+pqxjH3VMrF8j7d/rXd8p52Tsmy8AkEqMY/WSJXR6TLZxh+ftmZr38HfO3If6LbJdIX+LAdKM3bNLdtXTsvv28DM3ACQB3vHApEVuvUlyhaPl5Crwqa9m3D5zY4wCV1wj5Rd432HTWtmnH/a3KCCZuNYuxbj5IElm4eEyH/y0+w6dHYr84Buy3V0xf21JijzhCJoOZsmcdGZcXhMAEGOu0GkmH5yste7Jh4xvPiz3PrBWdsOrvtYCpBu76hlFfvJtRb78MUU++36Fb/qaIvf8Sva1VYkuDQAyEs0HTN4YV+cEPvw5mdp5PhaTPMyUcpl3f9B5bn/3P7Jj7b0H0pjr/30Tg7wHL4Hjz5C56Ar3HfbsUuTH35J1NVInyLa3SK+84HlmVhwvU+LIhwEAJBVT5Qidbt0v29PtbzGpom2/5Pi9MVV1/taSbCpmuTMvNqzxtRQg3dgdW978pLtTWr9G9i+/V+SxvySuKADIYDQfEDfmnR+QOeqERJeRUObkc6TDlnof9nQrcufP/C0ISBautUtT4jclZS54r8wJb3PfYdNa2V/dHNPxbPvUX6VIxLueU8+N2esAAOLMsXZJktTE6iVPrqkHSXI1czKEMUZmqffqJbtuNatigMnYucXzZlM73+dCAAASzQfEiTnxTJnz3pnoMhLOBAIKXPVJKcsRoLvqGdnVz/lbFJAMXIHTcZp8kN74Qf/qT0oLj3Dexz73N9n77ozJ69lIWPbJh7wPZ1Q5wyYBAElo6jQpL9/zyLJ6yZNtdPy+TJ0mU1jkbzFJyLl6qXWftLfR11qAdGFD7VLrfs8zU0fzAQASgeYDYm/BEpmrrpUxJtGVJAUzs1rmgvc6zyO3/4RxfWQUG4lIHW2eZyYOmQ8HPX9WtgKf+FdpZrXzPva+OxR59m+Tf7G1L7t/+Dn1HL5GAkAKMca4r9YndNqba/Ih01cu/d2iZZLx/nHcrl/jby1Auqjf6j7L0HXQAJBoNB8QW9NnKvCJL8u4rvTPUObcd7p/YG1vlb3nV/4WBCRSd6cUHvI+i3PzQZJMYZEC//g1qbjUeR/7q/+W3bR2Uq/jDJrOypI5gaBpAEg1ptI7dNp5hX+Gs47mg8nwsOm/M4XFkuNKbJoPwMTYeu+VSyorJ2sNABKE5gNiJ79QgU9fJ1NckuhKko7JylLgA592X930+AOyr6/zuSogQcYKWveh+SBJZvpMBT71VSk7x/sO4SFFfvTvsrt3Tej5bes+ae0q79c+6iS+TgJAKnJdSELmwyh2cFDa4/g3lObDCOfqpY1rZYccF2oAcLKuyQdWLgFAwtB8QGwEAgp8/Isys2YnupKkZeYcJnPmhc7zyK9vlh0c8LEiIEFczQcTkErKfCvDzF2owEc+J7nWH/V0K/KDG4Z3x46TffKvknUETZ923rifDwCQeK7JB3V2TOjfirS2u0GKOP4dpPkwwizxDp1Wf6+0bZO/xQDpwDH5YFi5BAAJQ/MBMWHe9zH3N88YYS65Uiqv8D7c0yh7/13+FgQkgG13hU2XyQSCvtZijjpR5t0fdN9h/15FfvhvsgP9UT+nDYdln3IETc+aLS1YMr4iAQDJocrRfJCYfngL5yqqrCypotLfYpLZ3IVSriPIfP1qn4sBUpsNtUttjry1WiYfACBRaD5g0syp5ypw+vmJLiMlmNw8Ba76pPPcPvh72V3bfawISADX5EOpPyuX3sqcfYnM6W9332HbJkVuvWk4KDsar77o/DWaU88laBoAUlVxmVTkvTbPEjp9MFfY9KzZMllZvpaSzExWlrToCM8zch+AcSJsGgCSEs0HTJq56IpEl5BSzNIVMiec4X0YDivyq5tlI2F/iwL85Go++JT38FbGGJnLPyYdcYz7Ti8/E3UwvDNoOjtH5oS3TaBCAEAyMMaMkftA6PSBCJuOnjP3YccW2e5OX2sBUpmt3+x9MGUaYdMAkEA0HzBpJujvmpR0YC77sFRc6n24Y7Pso/f7WxDgI9vhmApIUPNBGv46FvjYP0uz5zjvYx/8gyKP/WXM57H79kjrvNckmGNOliksmlSdAIDEcuU+ONcMZarGHd6303wYxbm61kakja/6WwyQwpxh0yk09bCro18dfYTNA0gvNB+ABDBFJTLv/Yjz3P7hNtn9e32sCPCRc/Kh3N863sLkFSjw6a+NWYe9/aeya1e5z598SLLW+/kJmgaA1OecfNgp6/j6n2lsZ4fU0eZ5xuSDhxmV0tTpnkfWcUEDAA+O5kMq5T3csqpZH/j9Fv3zAzt0x6v79Pr+XkX4twVAiqP5ACSIOe5U95qXgX5FbvsRP8QiPbkCpxM4+fB3Zkq5Av/4NWf4o2xEkZ9+R7ZhdDaLHRqUffph78dV1Q6HSgIAUppxhU739jiDTjOOK+9Bkqrq/KoiZRhjZJZ6Tz/Y9Wv4eQCIgg21pXzYdN9QRK/t7ZGVtLmlT3eubdEXHqzXB3+/RTc906QndoQU6mc9M4DUQ/MBSBBjjAJXfkLKzfO+w7rVss8/5mtNQLzZcFgKtXueJXLt0oHM7DkKfPyLUsDxT2R/ryI/+IZs61t+wFnzvPvXdtrbCZoGgHTgWLskSSJ0WpJkXSuXikulkjI/S0kZztyHlmapebevtQApKQ3Cptfu6dFgZHSzsaM/rMe2h/T9p5v0gd9v1pcerNdda/dra2sfUxEAUgLNByCBTPl0mUuvdp7b394yPLoOpItQu3MtUTJMPvydOfxomSs+7r5De4si/32jbF/PyE2RJx70vm9OrszK02JcIQAgEUxBkTRlmueZJXR6mGvyobqORrzLomWS4/fGrmf1EnAotn6L98HUaTIp0vRc1dR1yPtErLRxf69+8+p+fe4vO/She7bov57drafrQ+oaYCoCQHKi+QAkmDnj7e51LF2dsnfe4m9BQDy58h4kqTSxmQ9vFTjtPJlzL3XfYdd2RX76XdlwWLa5SdrwiufdzHGnyhQUxqlKAIDvXKuXCJ2WJNld3r8PhpVLTqaoRHKshrHr1/hbDJCC7A5H86EmNVYuWWu1qql73I9r6wvr0W0d+s5TTbrq7s3614fqdfe6Fu1o62NlG4CkQfMBSDATCCpw9aelYJbnuX3hcdm1L/lcFRAnHY68h6wsqajY31qiYN75AenoE913eG2V7J0/k33cMfUgyZxK0DQApBNT6R06bVm7JBsJS02O3wfCpsdklnjnPmjjq7JDQ/4WA6QaZ9h0aqxc6uwPK2uS785FrLR+X69+vWafPvPnHfrwH7bq5ud269mGTvUMMhUBIHG83+0E4CtTVSNz/rtl77vT8zxy248UuOFmmbwCnysDYsu2OSYfSqcm5SoGEwgo8KHPKtLWIm3b5Hkf+9hfpGDQ+wlq5kl1qXHFFQAgSq7Jh90NspGwTMDxb0ImaN4tDQ54Hplq76YNhpkly2X/fNfog75eafvr0oIl/hcFpADb0Sa1e1/gZFLk+/CSvCz9+KJ5agoNaFVTl15u6tbavd4ZENFq6R3SX7d26K9bOxQ00pKKAh1dWaijq4o0uyQnKX/2ApCeaD4AScK8/T2yLz0t7W4Yfdi6X/aPv5G5/KP+FwbEkmvtUhLlPbyVyclV4JNfUeRbX5D27/W+U9j7aiJz2rl8Yw8AacZU1crz7aDBAWnfXmlGpd8lJQ9X3oMJSLNm+1pKypm3UMrNk/r7Rh3Z9WtkaD4A3lx5D5JznVmyqizJUWXJVF24aKr6hyJau7dHLzV2aVVTt5q7Byf8vGErrd3bo7V7e/TL1fs0vSBLR1cV6ejKQi2bWai8yY5dAMAY+AoDJAmTna3A1Z9yh809er/s1o0+VwXEmGvtUhI3HyTJlJQp8I/XSwVF0T8oN1/muFPjVxQAIDFmznZ+v5bpuQ/W1XyYUSmTk+trLanGZGVLC4/wPCN0GnCzjpVLmjpNprjU32JiKDcroGOqivTx42bqZxfP1Q8vmKMPHVWhI2cWTHpF076eIT2wuV3/9nijrvzdZl3/yE79aWOrdoX6yYoAEHM0H4AkYuYvljn97d6H1iryvzfLDk38igcg0axj8sGUJVfYtBczq1qBa7/szGcZdf/jT2NVGgCkIZObK02f6XlmXXkHGcLVfDDkPUTFLFnufbB9s2x3l6+1AKnCuiYfUmzqYSzGGFWX5urixVP1jTNr9Ot3L9CXT63SufPLNK1gcgtNhiJWa/b06NZVzfrkfdv18T9t089e3KNVjV3qH4rE6FcAIJOxdglIMubSq2XXvCC17R992LRT9t7bpYuukMnO9r84YLJScO3SgczCw2U++GnZW2869H0JmgaA9FVZO5xv8FYZ3nxwrl2i+RAVs2SF90ovG5E2vSoddaLfJQHJz9F8MGnUfHirguygVs4u1srZxbLWamfHgFY1dmnV7m5taO5ReBLDC3u6BvV/r7fr/15vV07Q6IgZBTqqslBHVxZpVnFO7H4RADIGzQcgyZj8AgWu/IQiN9/oeW4f+L3s3/4sLV0uc8Qxw/+VTvG5SmCCUrz5IEmB489QZN9e2T/d7r7TnMNkaub6VxQAwFemskZ2zXOjbrcZvHbJ9vZILc2eZ0w+RGlmlTR1mtQ6+iIku26NDM0H4CC2vdX580U6Nx8OZIxRbVmuasty9c6l5eoeCOuVPd1a1TT8X1vv0ISfeyBsR57n52pWZXGOjq4abkQsrchXTpBlKgAOjeYDkITMkcfKHHuK7ItPet+hv1d6+VnZl58dvjqqboHMsmNllh0r1cwl4BZJyQ4OSN2dnmemNHWaD5JkLnivtG+P7LOPep+feq7PFQEAfFVV43373kbZocHh/f2ZZqzGS1Wtf3WkMGOMzOLlsk8/POqM3AfAgyvvQUqrtUvjUZgT1Ik1JTqxpkTWWm1v69fLTd1a1dSljft7FZnEVERT54CaNg7ovo1tyg0aLZtZqKPfmIqoKMrAf/cARIXmA5CkzOUfkV23WuqJYr/rjs2yOzYPX4ldNnV4GmLZsdLiI2Vy8+JfLBAN19SDJKVA5sOBjDHS1Z+Ubd0nbVp78GFFpcyxBE0DQDozVbXe63HCYWlvU0a+2e4Mm87Ll8orfK0lpS1dIXk0H7R/r2zzbpmKWf7XBCQpZ97D1OkyxSX+FpOEjDGaOzVPc6fm6d2Hl6urP6zVu7v18u4urWrqVkdfeMLP3R+2erGxSy82dknaq9mlOTq6skhHVxZq8fQCZQe5IBLAMJoPQJIyJVNk3vth2V/81/ge2N4q++RDsk8+JGVlS4uWvTEVcYwMP/ghkTrGaD5MSa3JB0kyWdkKfPIrsrf/VPa5vw3fWFWrwIc/NxxGCgBIXzMqpWBwuNnwFraxXiYDmw9q3OF9e3UdU7njYBYdKWuMZEe3t+z61TQfgAPYnY7Jh7rMnHo4lKLcoE6pK9EpdSWKWKutrX0jUxGv7+/zbqpHqaFjQA0drfrjhlYVZAd0TGWRjqsu0tFVhSrIDsbs1wAg9dB8AJKYOeFt0qbXZJ95ZGJPMDQovbZK9rVVsrdLqqodbkIsO1aau1AmwDcB8JFr8iE3XyavwN9aYsTkF8h8+LOy7/mH4SmlGVW8wQIAGcBkZUszqrwDphszM3TaNflA3sP4mOISqWaeZ4iuXbdGOv18/4sCktUOR9h0zTyfC0k9AWO0oDxfC8rz9d4jpinUN6SXd3fr5aZuvby7W539E5+K6BmM6In6kJ6oDykrIC2bUajjqoebEeUFrGcCMg3NByCJGWOkD/6jzFEnyj7/mOxrL0u93RN/wsZ62cZ62b/8Xioqljn8aGnZsTJLV8gUFMWucMCDbW/xPkihsGkXU1ImlZQlugwAgI9MVa2sR/PBNmVe6LS11p35UFXnay3pwCxZ7r1OZtOrsuGwTJALiADb3uqcrM6UsOlYKsnL0ulzSnX6nFKFI1ZbWvu0qqlLqxq7taW1b8LPOxTRcFNjd7d+8uJeHVaep5XVxVo5u0jVJTlcuAVkAJoPQJIzxkhHHitz5LGyQ0PS1o2yr74o++qL0p5dE3/irk7Z5x6TnntMNhCQFix9cyqCq7cRD67JhzRoPgAAMlClI3R6rODldNW6T+rt8Twy1Rm4gmqSzNIVsn+5e/RBb4+0/XVp/mL/iwKSDWHTcRMMGC2clq+F0/J1xbLpau8dnopY1dSl1bu71T0QmfBzv97Sp9db+vTrV/apsjh7uBFRXaTDpuUrGOA9CCAd0XwAUojJypIWHi6z8HDpPf8g29wk++pLw42I19dJ4aGJPXEkIm1aK7tprezvfiFVzHojJ+JYacGS4dUCwGQ5mg+mlOYDACD1OEOn9++V7e+Tyc3zu6TEcYVNS1IlzYdxm7tIysmVBvpHHdn1q2VoPgCy9Zu9D8orCJuOsbL8LL1tbqneNnd4KmLT/l6teiMrYnvb6K9T0WrqHNQfNrTqDxtaVZoX1HFVRVpZXaxlMwuUmxWI4a8AQCLRfABSmKmolDnrIumsi2R7e6T1a4anIta+JHV2TPyJm3fLPvwn2Yf/JOXlS0tXDDcjDj96eL0MMAHWNfmQgmHTAACoyjH5YK20u0GqW+BvPQnkyntQeYVMQaGvtaQDk50tLTxCWvvSqDO7fo100RX+FwUkGeuafKgl7yGeggGjJRUFWlJRoKuWT1dLz6BW7+7WS43dWrO7W71DE5uK6OgL669bO/TXrR3KyzJaMatQK6uLdUxVkYpzWTUHpDKaD0CaMPkF0tEnyhx9omwkIu3Y/OZ6pobtE3/ivl5p1TOyq56RNUaqW/DmVMTsOaxnQvQcO1lZuwQASEnTZkg5OdLAwKgj27hTJoOaD87JB8KmJ8wsWT58QdFbbX9dtqeLvDbA0Xwg78Ff5QXZOmtemc6aV6bBcERr9/bohV1den5Xl1p7J7aZoW/I6tmGLj3b0KWAkZZWFGhl9fBUREURWxmAVEPzAUhDJhCQ5i6UmbtQuuT9sq37ZV97SfbVl6QNazx/SI6KtcM/8Gx/Xfbe30hTpskc8UZOxKJlMrm5Mf11IM20OZoPpeX+1gEAQAyYQFCaVSN5BQNnWOi0a/LB0HyYMLNkufdar0hE2rhWOuoEv0sCkoZtbyFsOgllBwM6qrJIR1UW6WPHWm1t7dNzDV16flenGjom9h5ExEpr9/Zo7d4e3bKqWXOm5Or46mIdV12kOVNyuRgSSAE0H4AMYKZOkzn1POnU82QH+ofzHf4+FdG6f+JP3LZf9okHZJ94QMrOGW5ALDt2OLh66vTY/QKQ8mxfj9Tf63lmmHwAAKQoU1kj69F8sBkUOm0HB6S9Td6HVXW+1pJWZs2Wysql9pZRR3bDGhmaD8hkY4ZNs3YpGQSM0YLyfC0oz9dVy6erKTSg53d16oVdXdqwr9e7uRqF7W392t7WrzvW7ldFYZaOeyOwemlFAYHVQJKi+QBkGJOTKx1xjMwRx8he8XGpcYfsK2/kRGzbNDzdMBGDA9Lal2TXviT7G0nVc6Tlx6n/jPOUs2CJTJA9jRnNlfcgsXYJAJC6qhxhyk0N/taRSE0NkvXe8c3kw8QZY2SWLpd9+pFRZ3bd6gRUBCQPr6avpOGcmSLCppNRZUmOLl1SrkuXlKu9b0gvvrGaac3ubg1GJvYeRHP3kO7f1Kb7N7WpKCegY6qKdHx1sZbPKlR+NoHVQLKg+QBkMGOMVD1HpnqO9I7LZDs7ZNeukl59UXb9aqm3Z+JPvmu7wru2q/n+3ypQUqbC8y6VPeMCKYsdjRmJ5gMAIA2Zqhrvqzfb9mfMXn5n2HR2jlQxy9da0s7i5ZJH80H79sju2yMzfabvJQHJwO5wNB9YuZQSyvKydPb8Mp09v0x9QxGt3t2t5xs69WJjl7oGJhZY3TUQ0WPbQ3pse0jZAaPlswp0XHWxjqsqUlk+b30CicTfQAAjTHGpzIlvk058m+zQoLR5veyrLw2vZ2p2jNNHIRJqV+ddv1Bg8waZa78cw4qRKqyr+VBYLJOd428xAADESmWN+6xppzR/iX+1JIqr+VBZw+TrJDlzHyTZ9WtkTjvP13qApLHTFTbNyqVUk5cV0Amzi3XC7GKFI1br9/Xo+YbhqYjm7sEJPedgxOrFxm692NitH0laND1fx1UPT0VUlvCzJ+C3jGs+RCIRDQ4OKicnh2AaYAwmK1tafKTM4iOl935Ydk/j8EqlV1+UNq+TwuFxP2dk9XMK7G2SmVEZh4qR1Dz2FUti6gEAkNqmTJPyCzynRW3jTpkMaD7Yxh2et5tqx0oqRM0Ul0o18zzfaLXrV0s0H5CBhsOm2zzPTB2TD6ksGDA6YkahjphRqA8fXaEd7f1vNCI6ta2tf0LPaSVt2NerDft69avV+1RdkqPjZw/nRMwvz1OA9wWBuEv65kNXV5e++MUvav/+/br22mt1+umnj+vxAwMDevTRR/Xcc89p586d6u7ulrVWwWBQ1dXVWrlypc4//3wVFBTE5xcQhWuvvVb7948v9Pe2225TTg4dW/jHzKySmVklnX2xbE+3tH71cGj12lVSVyj6J9q1Q6L5kHlckw+lNB8AAKnLGDM8/bB14+jDTAmddk0+EDYdE2bJclmvq7w3vCobDjNdgszjWrkkDTfrkBaMMZozJU9zpuTp8mXT1Nw1qBcaO/V8Q5dea+7RBGMitCs0oLvXtejudS2akp+lldVFWlldpCNmFCg7SE4EEA9J33z40Y9+NO435v9u27Ztuummm7R3795RZ+FwWPX19aqvr9cDDzygz372s1q6dOlkyx23UCg04V8fkCimoFA65mSZY06WjYSl7ZuHGxGvvuj+AfQNtrNdXFuQgRzNB8PkAwAgxZmqWlmP5oNt2pmAavxlQ21SZ4fnGWHTsWGWLJd94PejD3q7pR2bpXmL/C8KSCBb771yibDp9FZRlK0LFk7VBQunqrM/rFVNXXquoUurd3epb2hinYi23iE9sLldD2xuV35WQEdXFerEmmIdU1mk3CwaEUCsJHXz4Te/+Y1eeumlCT1227Zt+sY3vqGenkMH5oZCIf37v/+7vvKVr2jJEn9Ho7dt2+br6wGxZgJBad4imXmLpEuvkm3ZJ7v2Rdn775I6PN5wDnn/gIr0Zr3+X5CksnJ/CwEAINYqHeuFGnfIWpveq17HuuiE5kNszF8i5eRIAwOjjuz6NcPfgwMZxNY7Jh9YuZQxinODOn1OqU6fU6qBcESv7unRcw2deqGxSx19418PLUm9QxE9Vd+pp+o7lRs0OqaqSCfV0ogAYiFpmw/333+/7r333gk9tq+vTzfddNNBjYcFCxbo/PPP18KFC5Wbm6utW7fqrrvu0pYtw/9wDQ4O6qc//am+973vKTs7Oya/hmgc2Hw455xzdPXVV0f1OFYuIVmZ8ukyp5+v8LrV0prnR9+hs93vkpAMXGuXmHwAAKQ4U1XjHQrc1Tn8fU/JFJ8r8o91NR9KpwznFWDSTHa2dNjh0msvjzqz61dLF16egKqAxLDWSo7mg6ml+ZCJcoIBHVNVpGOqihSOWL3e0qsXdg1PRTR1jm7aRqM/bPX0zk49vfPNRsTJtcU6mkYEMCFJ2Xy4++67ddddd0348X/84x9HVi0ZY3TVVVfpHe94x0FXHS1fvlyHH364vv3tb+uVV16RJO3evVuPP/64zjrrrMn9AsbhwOZDbW0tTQWkDVNc6vmDuGXyIeNYa1m7BABIX1VjBCs37kzr5gN5D/4wS1bIejQftG2TbG+PTH7i8gsBX7W3SqF2zyNTS95DpgsGjBZPL9Di6QX6wIoK7ero13O7uvR8Q6deb+mb0HMe2IjIy3pjIqKGRgQwHkn1N+Xv0wd/bzwYY5SVNb7+SFdXl/785z+PfH7VVVfpggsu8Bx3zsrK0ic+8QkFDwjpmuiap4navn37yMc1NTW+vjYQV8Vl3rcz+ZB5ujuloUHvM5oPAIAUZ4pLJcdV/jbNQ6ddkw/kPcSWWbLC+yASkTa96m8xQCLVb3afMfmAt6guzdW7l5bru+fV6X8unadPHDdDR1cWKiswsXWIfUNWT9V36ttPNunq32/Wd59q1LM7O9U/FIlx5UB6SZrJh46ODn3rW98amQQIBoP65Cc/qTvuuEP79u2L+nmeeOIJ9fUNdzTnz5+vd7zjHWPef+rUqTrssMO0YcMGScPTD34JhUIjvzZjjGprx7hqCkg1JY5Re0coIdKYa+WSJJXSfAAApIGqWmmjx5vAaRw6bcNhaXeD9+FY0yAYv8rZwxdseHxPZdevkVl+fAKKAvznDJueNkOmsNjfYpBSyguydd6CKTpvwRT1DIb1clO3nt/VpVWNXeoeHH/z4O+NiKfqhycijq0q0kk1JTqqspCJCOAtkqb5sGvXrpHGQ1FRkT73uc/p8MMP1x133DGu5ykpKdHJJ5+shoYGXXLJJVEFvE2Z8uYodH9///gKn4QDVy5VVlYqLy/Pt9cG4s6155e1S5nH1XwwRipN41UUAICMYapqZT2aD2k9+bC3URoa8jxi8iG2jDEyi5fLPvvoqDO7bo3/BQEJ4mw+sHIJ41CQHdTJtSU6ubZEg2Grdc09emZnp55t6FSof/yB1X1DVk/Wd+pJGhGAp6RpPvzdwoUL9ZnPfEbTpk2b0ONPPvlknXzyyeN6TEfHm2+G5ufnT+h1J+LA5sP8+YwIIr24Mh/U0yU7NCiT5V+wOxLLdjiaDyVlMgesvQMAIGVVOtanNu2UtTaqC6JSjTNsOhCQZs32tZaMsHSF5NF8UHOT7P69MtNm+F8T4CNrrbTDe+2SqV3gczVIF9lBo+WzCrV8VqGuOXaG1u6NZSMioGOrCnVSbYmOmkUjApkraZoPOTk5uvLKK3XhhRcqEPDvL+TQ0JC2bn2zez57tn/fKG/ZsmXk44ULF/r2uoAvSsrcZ10hqazct1KQYG0t3rezcgkAkCZMVa33RRd9vVLrfql8ut8lxZ+r+TCzWiabi0xizSw+0vv/Mb2xeunUc32tB/BdW4tzhS9h04iFYMC7EfFMQ6c6J9SIiBzUiDiuqkgn1hbTiEDGSZrmw4IFC7Rggf/d6pdffnkkI0KSli9f7ttrHxg2XVRUpLvvvltr165Vc3OzQqGQiouLNWPGDB199NE66aSTVF7Om7VIIa61S9Lw6iWaD5nDNflA2DQAIF24Jh8kqak+LZsPhE37y5SUSbPnSA3bR53Z9aslmg9Idzu3uM8Im0aMeTUint4Z0rMNXRNuRDxRH9IT9SEaEcg4SdN8SJT7779/5OO8vDytXLnSl9dtb29XS8ubVwP/53/+56j7tLa2qrW1VRs2bNAdd9yh888/X+973/uUlZXxf2xIBUXFkglI1iO8idDpjGIdmQ+G5gMAIE2Y/AJp6nSpdd+oM9tYL3PEMQmoKs5ceRaETceNWbJC1qP5oA2vykbCMgHWWSJ92R2O5sP0mTKFRf4Wg4xycCPC6rU4NCJOqi3WUZWFygnSiED6yeh3sV9++WVt3Lhx5POzzjpLhYWFvrz2gSuXDpSVlaXp06crJydHe/bsGQnADofDuu+++9TY2KgvfvGLMVtN1dHRoVAoFPX9A4GAqqqqDrotGAz6uioLqSJL/cUlUqh91EmgO6QgTbSMEelo81wTEJg6nWYqACBpBN+SQ/TWzw/FVtUq4tF8MLsb0u7fO9vTpbDHr1WSgrXz+D4vTiJHHK3BB+8ZfdDTpeCuHQrMZZUv0tfgzm3eP1PUzU+7r7FIXlmSjpldqmNml+qTEau1e7r15I4OPbszNMGMiDcbEflZAa2cXayT60p1dFXRIRsRk/2+BZkj0dljGfsVenBwUL/61a9GPi8sLNQll1zi2+u/tfkwf/58XXbZZVq2bNnIG/nWWj3//PO64447tHv3bknDDZNHHnlEZ599dkzqePDBB3X33XdHff/i4mLdeuutB902fXr6jZEjNvZMKdegR/OhMBJWSUWF/wUhIZpC7fL6Nqykpk5F/H8AAEhS41152n7YYnWufWnU7cG9japIs3/v+l9rVLPjbPryY5Q1Pb1+vcnClp2uxh/kyg70jzrL37FJpcefkoCqgPiz1qqpYZvnWfHhK/jZEglTOVM6d7k0FI7opZ1tevj1Zj22eb86egfH/Vy9QxE9tr1Dj23vUGFOUCfPm6azFlbohDlTlZt16MYCq9qRrDK2+fDb3/525A19SXrPe96jkpIS315/06ZNIx+fcsopuvbaa0d1KY0xOv7447V48WJdf/31ampqkiTdc889OuOMM+juI+kFyqZK9VtH3R5xrOFB+rHhsMKOwOng1Gk+VwMAQPxkOwJPB3dulw2HZdLoisSBHZs9bzeFxQpOm+FzNZnD5OQq9/Cj1Pfys6PO+l9+Xrr8IwmoCoi/8P69zp8hc+Yv9rkaYLSsYEDHzynX8XPK9S9nHdCIeH2fOvqGxv183QNhPbhhrx7csHekEXH2wgodH2UjAkgmGfnu9caNG3XfffeNfH7YYYfpvPPO87WGz3/+82publZra6uWL18+5nhUaWmpPv/5z+sLX/iCIpGIWlpatGbNGh1zTBrujkVaCZZO8bw93NHmcyVIlEhHmxTxHj8NTmVqCgCQPrJdgaeDAxras0vZaZSFMLjdu/mQM2dBwkf7013uipXezYeNryrS061AgT9rhAE/DWzZ6DzLmbfIx0qAQzu4EbFwuBGxqVmPbY5NI+KUNyYiaEQgVWRc86Grq0s/+MEPZO3wtsC8vDx96lOf8j2zoKioSEVFRZo7d25U9589e7aOPPJIrV69WpL00ksvxaT5cO655+qEE06I+v5ev0/79u0b+f0EDhTOK/C8va95j5qbXcP6SCcRx5WRktRmjQz/HwAAkkQwGDxoZUFLS4vC4ej3N9u8QskYyeP74v2vvKxgdn5M6kwGA5u93wgcrKjke7w4i9Qu8D4Ih7X3qUcVXL7S34IAHwy9MnqlnSSpYpb29/ZJvX3+FgSMw9xC6WNHletDy6fqld1deqo+pGfqQ+oaGH9GRPdAWA9s2KsHNuxVQXZAp86fPtKI6OpoH9f3LcgcxpiErszPuObDT37yE+3fv3/k84985COaOXNmAiuK3qJFi0aaD9u2ee87HK/S0lKVlpZO6jnC4bAikUhM6kGaKfJeZRbpaNPQ0Pg7/kg9tsU7jFLBLA3lFcjw/wEAIEmFw+Hxfb8SCErTZ0nNTaOfq2G7bJq8KWwjEdldO7zPqmr4Hi/O7MxqqXSK5DFJHH71JdnDj05AVUB8hbe/7nm7qZnH1xyklCNn5OvIGfm65pgKvbqnW0/v7NRzDZ3qGhj/e2o9g5GRRkRRbpZOrCnWqbVFWlpRoABTiDiA3xfcv1VGNR/+9Kc/6YUXXhj5/IwzztCpp56awIrGp7i4eOTjAxsoQLIyJWXeB50dvtaBxLGOvAeVTpFJ8D+AAADEXFWNZ/NBjfX+1xIvLc1Sf6/nkamq87eWDGSMkVm8XPa5v406sxvW+F8QEGfWWs8cQUmSI2sHSHZZAaOjKot0VGWRPnHczEk3Irr6h/TQ5jY9tLlN5QVZOrW2RKfPKVHdlLw4VA+MT8Y0H9avX6877rhj5PO6ujp9+MMfTmBF49fT0zPycW+v9zf8QFIpdkzVdHbIWstO4EzQ4QgXL5vqbx0AAPjAVNXKrn5u1O22aWcCqokTx9SDJCmNci2S2tLlkkfzQXsaZVuaZcorfC8JiJvW/c6L14wrawdIIbFuRLT0DOkPG1r1hw2tqi3N1alzSnRaXYmmF2bHoXrg0DKi+dDc3Kzvf//7I7vPioqK9M///M/KyclJaF3WWoXDYWVlRffH0Nr65pt4BQXeu/SBZGIcgdMaHBi+Ys6RCYE00k7zAQCQQSodb77vbZQdHJTJTv0f/F0rlzR9pkxe+uRaJDOzeLlciXt2/RqZU87xtR4gruq3uM+YfECaObAR8fFjZ2rt3m49Vd+p53dNrBFR39GvX6/Zp1+v2aelFfk6fU6pTpxdrKJcgqrhn7RvPvT09Ojb3/62Ojs7JQ3vufqnf/onVVQk7mqQV199VY899pheffVVvfvd79Z5550X1ePWrl078vG0adPiVR4QM8Y1+SBJoXaaDxnAOpoPppTmAwAg/ZiqGu83hSMRae8uqXqO3yXFnG3c4X1QXednGRnNlE4Z/v32agStXyPRfEAasa6VS9NnyhQU+VsM4KPs4JuNiMHwARMRuzrVPYFGxLrmXq1r7tVPX9yroysLdfqcEh1TVaScIOuQEV9p3XwYGBjQd7/7XTU0NIzcduWVV2rZsmUJrEraunWrnnrqKUnSs88+G1XzoaGh4aBfx5IlS+JWHxAzrswHSQp1SBWVvpWCBHFNPkwp97cOAAD8UFEpBbOk8OgAVNu4UyYNmg/a5Z1fQd6Dv8ySFZ5TKHbDK7KRsEyAq1qRHmz9Zs/bTd0CnysBEic7aHR0VZGOrirSJ95oRDzT0KXndnWpq398oetDEavnd3Xp+V1dKswO6ISaYp1WV6LDZxBUjfhI2/bW0NCQvv/972vdunUjt5166qm68MILE1jVsBUrVox8vGHDBm3cuPGQj/nFL35x0OcrV66MeV1AzOXmyeTmep8ROp0Z2l2B00w+AADSj8nKkmZWeR+mQe6D7e/3DtSWZJh88JVZutz7oLtTqt/may1AvBA2DYz290bEZ0+u1gPXnqRvX3y4zlgwXVmB8TcOugcjenhrh657pEEf+cNW/fLlZm1v6xv+uwfESNo2H370ox9p9erVI58fdthhuuaaaxJY0Zvq6uo0f/6bwUg///nP1dXV5XnfSCSiW265Ra+99trIbQsXLtSiRYviXicwWcYYBRxvMtvOdn+Lge/s4KDUFfI8M2Q+AADSlHGELttG74mBlNK0U3K9IUHzwV/zl0hZ3hkidv1qz9uBlNO63/3zRA3NByA3K6i3HVah71xyhH7z3kX61MqZOnxGgSYyv9DSOxxU/U9/3qF//L/tuntdi/Z1D8a8ZmSetFy79Mwzz4ysNZKkqVOn6jOf+YystRoYGDjk47OyshQIjO7LXHbZZSMfL1myRF//+tcnXOPll1+ub37zm5KGVypdd911uuyyy3TkkUeqoKBAg4OD2rRpk37/+98fNL2Rn5+fNE0UIBrBsikKN+8efRBi8iHthdrcZzQfAADpqrLG+/Z0mHzYtd37ICdXmj7D32IynMnJlQ5bOpzx8BZ2/RrpHZeNuh1IOY6VS5KYfADeoignqLPnl+ns+WXa1z2oJ3eE9PiOkHa094/7uXZ2DIwEVS+Z/kZQdU2xigmqxgSkZfPhvvvuO+jz1tZWffKTn4z68ddff72WLl0a67IOsmzZMl188cW69957JUmNjY266aabJA03P4aGRu9sKy4u1mc/+1lVV1fHtTYgllyTD6xdygCuvAeJ5gMAIG05Q6f37ZHt75PJzfO7pNhxTW9U1ZIxkABmyYrhRsNbbd0o29crk5fve01ALDnDpitmETYNjGF6YbbeubRc71xarh1tfXp8R0hP7Ahpf8/48iEkaf2+Xq3f16ufvbRHR1cW6bQ5JTqWoGqMQ9o1H/r7+7V1q+MfqCRz5ZVXqqSkRHfeeacGB98cZXpr48EYo5UrV+rqq6/WtGnT/C4TmJRg2RTvA5oP6c/VfMjJlfIL/a0FAAC/VHqvXZIkNTVIc1I3JNUr4Fhyr5pCfJkly70bXeEh6fXXpGXH+l0SEFO2fovn7aZ2vuftAEarm5Knuil5umr5dG1o7tVjOzr09M5OdQ9ExvU8QxGNBFUXZAd04htB1UsrChScQN4EMkfaNR86OzsTXcK4XHjhhTrhhBP0wAMPaPXq1dq7d6+MMSouLtaMGTN0xBFH6Pjjj1dlZWWiSwUmxJn5EGr3txD4zrrCpsumyhi+OQEApKlpM6ScHMlj3attqpdJ0eaDtVZq3OF9SN5DYlTVSiVlksf31Xb9GhmaD0hhw2HT3s0H0XwAxi1gjJbOKNDSGQX62DEztKqpW49tD+mlxi4NRsYXMN3zRlD1w1s7NDU/S6fWlei0uhLNmZLLz/oYJembDz/84Q/Hdf9p06bprrvuikst8XreadOm6f3vf7/e//73x+X5gUQKutbrMPmQ/lyTD6xcAgCkMRMISLNqvN80S+XQ6Y5Wqcv7Qi9D8yEhTCAwPP3w3GOjzuw6QqeR4lr3ub/mkPcATEp2MKDjZxfr+NnF6hoI67mGTj22PaTX9vZ4T9SNobV3SH/c0Ko/bmjV7NIcnV5XqlPrSlRRlB2X2pF6kr75ACC1BWg+ZC5H88G4ckAAAEgTpqrWc12IbUzh0GnHyiVJw1fgIzEWL5c8mg/as0u2dZ/M1Ol+VwTEhmvqQZJqaD4AsVKUE9RZ88p01rwy7e95M6h6e9v4g6obOgb061f26devDAdVnzanRCfVlBBUneFoPgCIK2fmQ1dINhyWCfKPULqyHUw+AAAyVFWN9+1NqTv54Mp7UFm5TFGJr7XgTc7cB72xeunks32tB4gVu8PRfKiolCkgPw6Ih2kF2bp0SbkuXVKune39enxHSI9v79C+SQRV//ylvcNB1XUlOqaqSLlZBFVnGpoPAOLKlfkga6XukFTiaE4g9TnXLpX7WwcAAD4zlbXebwi3t8p2d8oUFvtd0uS5VkZVM/WQSKZs6vDkidefz/o1Es0HpChbv9XzdlYuAf6oKcvVVcun68ojp2nDvl49vj2kp3eG1DXJoOoTZhfrtDklOpyg6oxB8wFAXDkzHyQp1EHzIZ2NETgNAEBaG2sNUeNO6bCl/tUSI67JB1NV52sdGM0sWS7r0XywG9bIRiLDOSRACrHWSjsJmwaSQcAYLa0o0NKKAn30mAq93NStx3aE9OKuiQVVP7KtQ49sezOo+ow5Jaqbkhen6pEMaD4AiKtASZn7kNyHtGX7eqXeHs8zQ/MBAJDuyqZKBYVST/eoI9tUL5NizQc7NCjt3uV9SNh0wpklK2T/eu/og65OqWEbb9Yi9bQ0u8Om6/j/GUiU7GBAK2cXa+XsYnUPhPVsQ6ce3x7S2kkGVc+fmqcz55Xq1NoSFZEPkXZoPgCIK5OVpUBxqSIejQYbahdDdmmqo819RvMBAJDmjDFSZa20Zf3ow1QMnd7TKIW99z0bmg+Jt2CplJUtDQ2OOrLrVsvQfECqcaxckiTNnutfHQCcCg8Iqm7pGdST9SE9tn1iQdVbWvu0pbVP/7OqWStnF+mseWVaNoO1TOmC5gOAuAuUTvFsPjD5kMZceQ+SVErmAwAg/ZmqGlmP5oNNwdBpZ9h0MEuaWeVrLRjN5OZKC5ZIG14ZdWbXr5HOf4//RQGTYOsdK5dmVBE2DSSh8oJsXbK4XJcsLtfOjn49vj2kJ3Z0qLl7fEHVgxGrp+o79VR9p8oLsvS2OaU6c16pZhXnxKly+IHmA4C4C5ZN1ZDXD600H9KWdeU9FBQO/4AMAEC6c+U+NO2UtXZ4OiJVuMKmZ1XLZGX7Wws8mSXLZT2aD9qyQba/TyaXfdpIHa7mA2HTQPKrKX0zqHrjvl49viOkp+tD6hxnUHVLz5B+t65Fv1vXoqUV+TpzbqlOrClRfjY5RqmGPzEAcRdwrdmh+ZC+XJMPpaxcAgBkBlPpaD50dUqhdl9rmSx32PQYwdrwlVmywvsgPCS9/pq/xQCTYK11r12i+QCkjIAxWlJRoE8cN1O/eOcCffm0Kp1UU6yc4PgvvljX3KsfPLdHH7xns37w7G6ta+4Z/lqBlMDkA4C4C5ZN8bzdptgP3hgHV/OBvAcAQKaorHGfNdZLpd7fHyUl19ol8h6SR3WdVFzqeXGPXb9G5ohj/K8JmIj9e6VuR9h07QKfiwEQC9lBo5XVxVpZXayewbCe2dmpR7d1aF1z77iep2/I6pFtHXpkW4cqi7P1trmlOmNuqaYVMIWZzGg+AIi7gOtqd5oP6avDu/lgaD4AADKEKS6RSso8v9+xTfUyS5b7XdKE2K6Q5FinSNh08jCBgMzi5bIvPD7qzK5bnYCKgAnaOUbYdA1h00CqK8h+M6h6d+eAHn2jmdDSM758iKbOQd32yn7d/up+HTmzUGfNK9Vx1UXKCbLkJ9nQfAAQd67JB9YupS9n5gPNBwBAJqmq9b7YonGn76VMmCvvQWLyIdksXS55NB+0u0G2db/M1Gm+lwSM15hh0/kF/hYDIK5mFefoyiOn6/IjpunVvT16ZGu7nmvo0mAk+pVKESut3t2t1bu7VZQT0Kl1JTpzbpnmTc1NrXytNEbzAUDcOScfaD6kL+fapXJ/6wAAIIFMZY1nCLAd6w39JGN3OWotKibLKcmYxcvlervGbnhF5qQzfa0HmAi7wxU2Pd/nSgD4JRgwWjGrUCtmFaqrP6wn60N6eGuHtrT2jet5ugYi+vPr7frz6+2qK8vVmfNKdVpdiUrzePs7kfjdBxB3QdfV7v19sv19Mrl5/haEuLLWOpsPrF0CAGQUVyBzU4NsJCITSIHVAI07vG+vquOKwiRjppQPZ400eUzWrF8t0XxAkiNsGkBRblBvP2yK3n7YFNW39+uRre16bHtIHf3hcT3PjvZ+3bqqWb9a3axjqop01twyHVVZqGCA7138RvMBQNwFXGuXpOHpB5oP6aWnWxoc8D7jCkkAQAYxlTXeV6L390qt+6RpM/wuadysI2yavIfkZJaskPVoPtj1a1Kn4YXMtX+v1NPlecTkA5B5asty9aGjZ+iq5RVa1dSlR7Z16KXGLo1jK5OGItJzDV16rqFLU/KCOn1Oqc6cV6rZpbnxKxwHofkAIO6CY73h3NmREj94YxxcK5ck1i4BADJLZY37rHFn0n8PZCNhd+aDa6oDCWWWLJd9+N7RB10hadd2qYarx5HEXHkPxhA2DWSw7KDR8bOLdfzsYrX1Dumx7cMh1Q0djoseHdr6wvrDhlb9YUOrDivP01nzynRybbEKc4JxqhwSzQcAPjCFRVJWtjQ0OPowRO5D2ulwhE1LUmmZb2UAAJBoJr9AKq+QWppHndmmepkjj01AVeOwb6800O95ZKrn+FwMonLYUikrSxoaGnVk162RofmAJGZdK5dmVBI2DUCSNCU/S5cuKdcli6dqc0ufHtnWoSd3hNQ9GBnX87ze0qfXW/bollV7dcLsYp05r1RHzChQgJWSMUfzAUDcGWMULJuq8P69o85sZ7v40p5erGvyobhUJivb32IAAEi0yhrP5oNzoiCZOFYuyZixpzqQMCY3T5q/RNr46qgzu3619PZ3JaAqIDrWMflgali5BOBgxhgdNi1fh03L14eOqtBzDZ16ZFuHXt3T473y0mEgbPX4jpAe3xFSRWGW3ja3VG+bW6oZRTlxqz3T0HwA4ItA6RTP5oM6mXxIO22OyQfCpgEAGchU1cqufWnU7bbRIxQ4yVhX2PT0WTK57EpOVmbJclmP5oO2rJft7+fPDklpzLDpOpoPANxyswI6bU6pTptTquauQT26vUOPbuvQ3i6P7RtjaO4e0p1rW3Tn2hYdMaNAZ80r1Qmzi5WbRV7SZPC7B8AXQdcbz6F2X+uADzockw/kPQAAMlGVY0JgT4NsOOxvLePkCpsWYdNJzSxZ4X0wNCRtfs3fYoBojRk2zbowANGpKMrW5UdM008umqtvnjVbZ8wpUU5w/Ps21u7t0U3P7NYH79miHz6/Wxv39Q43STFuTD4A8EWgbIr3AZkPace1dskw+QAAyECmstZ7/H9oSGreLc2q9ruk6DmaD4bmQ3KbPUcqKhkOmX4Lu26NzOFHJ6Ao4BAImwYQQwFjdMSMQh0xo1AfOzasp+o79cjWDm3c3zuu5+kZjOihLR16aEuHqktydObcUp0+t1RT83lLPVr8TgHwRbDU+41n29nubyGIP1fmg+P/AQAA0tqsaskEJOsRhNi0M2mbD7avV9q3x/OM5kNyM4GAzOIjZV98ctSZ3bDG/4KAKNgdjubDjCqZPMKmAUxcQXZQ58wv0znzy7Qr1K9Ht3bo0e0htfUOjet5doUG9Ks1+/TrV/bp6MoinbegTCtmFSoYIMl0LKxdAuAL5+QDmQ/px7V2aQrNBwBA5jHZOdKMWZ5nNplDp8eqjeZD8lvqWL3UWC/b7sjnAhLI7vTOe2DlEoBYqi7J1dUrKnTrJfN03enVOrGmWOONdIhY6cXGLt342C5dc+9W/XbtfrX0jC9fIpMw+QDAF67JB5oP6cVGIlJHm+eZKSXzAQCQoSprpT2No262TcnbfHA2RnLzpPIKf4vBuJnFy73XfUmy69fInHimr/UAYxkOm3ZMPtQSNg0g9oIBo2OqinRMVZFCfUN6fEdIj2zr0Pa2/nE9z76eId3+6n7duXa/jq0anoZYPqtQAcM0xN/RfADgi7EmH2wkIhNgECstdHVIrvBMMh8AABnKVNbIvvzM6IPGnf4XEy1X2HRVLd+3pQAzdZo0a7a0u2H04fo1Es0HJJN9e6Sebs8jQ/MBQJyV5GXpwkVTdeGiqdrW2qdHtnXo8e0d6hzwWJnpELHS87u69PyuLlUUZuuc+aU6a16ZppANQfMBgD+ckw+RiNTTNRyKh9TnynuQaD4AADKWqarxvgq9uUl2cGB4NVOSsY07PG8n7yF1mCXLZT2aD3b9Gi7+QVKx9d4rlwibBuC3uVPzNHdqnj64YrpeaOzSI1s7tHp3tyKucUIPzd2Duu2V/brj1f1aObtY584v07KZBRk7DUHzAYAvnJMP0vDqJZoP6cHVfAgEpGL+jAEAGaqq1vv2SGR4HdPsOf7WcwjWWvfkA82HlGGWrpB95L7RB50dw3++vKmLZOFauTSjSiYv399aAEBSdjCgk2pKdFJNiVp6BvW37SE9srVDTZ0DUT9H2ErP7OzUMzs7NbMoW+fOL9Pb5pWqLC+z3o7nUgcAvgiOddV7iNyHdGFdzYfSqTKBoL/FAACQLKbPkrK8f9BMytDptv3uFShVdf7Wgok77HAp6Pj/bsMaf2sBxmAdzQdTx8olAIlXXpCtdy8t148unKN/P7tGp9WVKDswvimGPV2D+tWaffrwH7bou0816tU93cMXe2QAmg8AfGGyc6T8Qs8zG2r3txjET3uL9+2sXAIAZDCTlSXNrPY+TMbQ6bEaItWOKQ4kHZObJ81f7Hlm1632uRrA23DYtGPtUu08f4sBgDEYY7S0okCfO6lS//PO+frQURWqLB7f6syhiPRUfaeue6RB1963XfduaFWo35GbmSZoPgDwjSkp9T7obPe1DsTRGJMPAABkMlPp/aa9TcLQaetauTR1mkxBka+1YHLMkuXeB5vXyw70+1oL4GnfbqnXMWlVw+QDgORUkhvUxYun6kcXztE3z5qtk2uLlTXOd9mbOgf0Py8360P3bNF/Pt2kdc09aTkNkVlLpgAkVkmZtLdp9O2drF1KF661S4bJBwBApquq8b49GdcuuZoPrFxKOWbJctk//Hr0wdCgtHm9tHSF/0UBByBsGkAqM8boiBmFOmJGodr7hvTo1g49uKVde7oGo36OwYjV4ztCenxHSLNLc3Tu/DKdMadURbnpsbqayQcAvjHFZd4HZD6kjw7H5APNBwBAhjOu0OmWZtm+Hn+LOQTX5IMhbDr11MyVioo9j+x6Vi8hCbjCpmdWEzYNIKWU5WXpnUvL9eOL5uqGt83WiTXFCo4vGkINHQO6ZVWz/uEPW/T/nmnShn2pPw3B5AMA/zjWLlnWLqUP19qlsnJ/6wAAINlUOiYfJKmpQZq70L9axmAHB6U9u7wPaT6kHBMIyixeLvvik6PO7LrV0nsSUBRwALvDETZdy8olAKkpYIyWzyrU8lmFausd0sNb2/XQlg41d0c/DTEQtvrb9pD+tj2k2rJcnTu/TKfPKVFhTupNQzD5AMA3pqTM+4C1S2nBDg05/yxZuwQAyHjlFVJunueRTabVS3t2SZGI5xGTDylq8ZHetzfWO1dmAn6w1ko7t3kfEjYNIA1Myc/Sew6fpp9ePFfXn1GtldVFCoxzGqK+vV8/e2mv/uGeLfrv53br9f29KTUNweQDAN+wdinNhdok1z+ANB8AABnOBALD0w/bXx992JQ8odPOsOmsLKmi0tdaEBtmyQq53qKwG16ROeEMX+sBRowVNs3kA4A0EjBGR1UW6ajKIrX0DOrhrR16aEu79vcMRf0c/WGrh7d26OGtHZozZXga4rQ5JSrITu5pCCYfAPjHsXaJyYc0MdaVczQfAACQcaxesknUfHCGTc+aLZPFtWupyJRPl2ZWex+S+4AEcq1ckglIs+f4WwwA+KS8IFvvPWKafnbxPF13erWOrRr/NMT2tn795MXhaYgfPr9bW1r64lNsDPDdIwDfmJIp3ge93bKDgzLZ2f4WhNhyNR+ysqWCIn9rAQAgGblCpxuTp/lA2HR6MkuWy3pkedhXXpBd9Yx01AkyZpzvfACTVb/V+/aZVYRNA0h7wYDRMVVFOqaqSPu6B/XXre3665YOtfZGPw3RN2T10JYOPbSlQ/On5uncBWU6pbZE+dnJM2+QPJUASH+uyQdJInQ65dkOR/NhSjk/zAIAIMlUOUKnO1plu0L+FuPSuMP7dpoPKc0sWeF90NujyE/+Q5H/+rrs3iZ/i0LGs/WETQOAJE0vzNYVy6brlkvm6cunVunoykKN912ULa19+uHze/QP92zRT17Yo+1tyTENweQDAN84A6el4dVLU6f7VgvioK3F+/ZSVi4BACBpOPPBpWmndNjh/tXiwXZ2SB1tnmdMPqS4hYdLwSwp7Liact1qRb7+KZlz3ynz9vfI5Ob6Wx8yjo1EpJ2OyYc6mg8AMlMwYLRydrFWzi7W3q4B/XVLhx7e2q62vnDUz9E7FNFfNrfrL5vbdVh5ni5eUq7LZs6MY9VjY/IBgH8KiqSA48sOodOpz7F2yZD3AADAsNKpzlWENhlWL7nyHiSpqs6vKhAHJi9fOvK4se80NCT7f3cpcv0nZdc8J2tdMdVADOzbI/X2eB6Z2nk+FwMAyWdGUY7ev3y6brl0vv7llCotn1kw7ud4vaVPP3+pOQ7VRY/mAwDfmEBAKvZevWRZu5TynGuXaD4AACBJw2sIXauXmur9LcaDda1cKi6VxppgRUoIvPuD0vQornxsaVbkh/+uyH/fKNu8O+51ITO5Vi4Nh03P9bcYAEhiWQGjE2qKdcOZNfrJRXP1ziVTVZobTHRZUaP5AMBfjuaDOpl8SHmuwGmaDwAAjDCO0GnbmPjmg3PyobqO/KY0YKbPVOCr/ylz5oXDb/AeytqXFLn+U4r86XbZgf74F4jM4mo+zKqWyc3ztxYASBGzinP0gRUVuvXS+frCyZVaNmP80xB+I/MBgL9czQfWLqU+Z/Oh3N86AABIZpXezQc17pS1NqFv8ttd3g0Qw8qltGEKimQu/6jsSWcp8psfS1s3jv2AoUHZ++6Ufe4xBS7/qMyyY/0pFGnP1nvnPbByCQAOLTtodHJtiU6uLVFjaEAPbWnXI9s61NkffTaEX5h8AOArU1zmfcDapZRmB/qlni7PMzIfAAB4k3GtXerpklwrDH1gI+Hh0GsvhE2nHTN7jgJf/A+ZD37GfXHQgfbtUeS/b1T4h/8mu39v/AtEWhszbLqWsGkAGI+qkhz9w1EV+p9L5+lzJ87S0or8RJd0ECYfAPirxJX5wORDSnNNPUjD4ZoAAGBYpaP5IEmNOxM3Mdi8Wxoc8DwyNB/SkgkEZE46U3b5Stl7b5N97AHJRsZ+0JrnFVm3Wub898ic+06Z7Gx/ikV6ad49Rtg0zQcAmIicYECnzSnVaXNK1dDRrwe3tOtv2xL/XhuTDwD85QorZO1Sahur+VA2xb86AABIcqaoxNmYT2jugyvvwQSkWdW+lgJ/mcIiBa74uAJf+b4057BDP2BwQPbe3yjy9U/JvvZy/AtE2iFsGgDia3Zprj5y9Az9z6Xzdc0xMxJaC80HAP5yZj60+1oGYsu61kTk5cvkJX8AEgAAvnKtXmpKXPPBupoPMyplcnJ9rQWJYWrnKfAv35G5+lNSUfGhH9C8W5H/+rrCP/6WbMu++BeI9OFauTSrWiaXrzcAECu5WQGdXFeS0BpoPgDwlTvzoUPWWl9rQQwRNg0AQNSMI3TaNjoyF3zgaj6wcimzmEBAgVPOUeDGH8ucep4UTQD6y88q8rVrFfnL3bJDg/EvEinP7vCefGDlEgCkH5oPAPzlyHxQeEjq7fa3FsROe4v37YRNAwAwmmvyYXfDcBBrIrgmH2g+ZCRTVKLAVdcq8K/fiy4AeKBf9p7/VeSGf5Rdvybu9SF1ETYNAJmF5gMAf7nWLknkPqQyx+SDofkAAMAopsp78kH9fVJLs7/FSLK9Pc7XZfIhs5k5CxT48ndl3n+tVFB06AfsaVTkpq8p8tPvyLbuj3+BSD3NTVJfr+eRqZ3nczEAgHij+QDAX2M1HzppPqQq61q75AjUBAAgo82a7T5rSsDqpbGCrl2NEmQMEwgqcNp5CnzzJzInnx3VY+xLTw2vYnrwD7JDQ3GuEKnE1jumHgibBoC0RPMBgK9Mbp6Um+d92Nnuay2IIWfmA80HAADeyuTlS9NmeJ7ZsRoBceIMm87Ll8orfK0FycsUlyjwgU8r8C/fkWqieJO4v0/27l8o8o3PyG58Nf4FIjXUe+c9qHI2YdMAkIZoPgDwn2P6wbJ2KSVZa6UOx9qlKQROAwDgqdKR+5CI0OnGHd63V9fJRBM4jIxi5i1S4Cvfl7niGim/8NAP2N2gyPe/qsjPv+eelkXGsI7mg6lh5RIApCOaDwD851q9xNql1NTbM7yj2gtrlwAA8GQcodO2KXkmH8h7gIsJBBU44x0KfPPHMieeGdVj7AtPKHLdJxT5672y4XCcK0QyGg6b3uZ9WEfYNACkI5oPAPxXUuZ9O2uXUpNj6kESa5cAAHCpdGQp7Nnl6458a60786Gqzrc6kJpMSZkC//AZBb70H1I0zaq+Xtm7blXkxn+SfX1d3OtDkhkzbJrmAwCkI5oPAHxnHM0HG2r3tQ7EyFjj80w+AADgybiCnIeGpH27/Sukdd/wFKMHU03YNKJj5i9R4Ks3yVz+USm/4NAPaKxX5Lv/qsj/3CQbaot/gUgKdocj78EEpOo5/hYDAPAFzQcA/mPtUlpx7u4tKpbJzva3GAAAUsXMKing+HHMz9BpV9i05J7OADyYYFCBMy9U4Bs/kjn+9KgeY5/9myJf/YQij9zPKqZMUL/V+3bCpgEgbdF8AOA/V/OBwOnU5Go+lBE2DQCAi8nOkSoqPc+sj6HTrrwHlVfIFEQRJgy8hSmbqsCHP6fAF/7dHax+oN4e2Tt/psi/fU52y4b4F4iEsTsdYdOsXAKAtEXzAYD/mHxIL+0t3reT9wAAwNiSIXTa1XwgbBqTZA47XIHr/p/Mez4k5eYf+gEN2xX59pcU+eV/sY41DdlIRKonbBoAMg3NBwC+c2U+qLvT14BFxIZr7ZIh7wEAgDEZ11ojPycfHCueDM0HxIDJylLgnEsU+OaPZI47NarH2KcfUeS6Tyjy2J9lI6xiSht7m6R+R9h0zTyfiwEA+IXmAwD/uSYfJKkr5F8diI0O19olmg8AAIzFGTrdvFt2oD/ur28HB6Q9jd6HVXVxf31kDlNWrsBH/1mBz90ozZp96Af0dMv+5ieK/Ns/y27bFP8CEXe23hE2HQhIswmbBoB0RfMBgP9Kxmg+sHop9ZD5AADAxDjWLslGpD274v/6TQ3Dr+WByQfEg1l8pAJf+38y7/qAlJt36Afs3KrIf3xRkf+9WbaTi5RSmqv5UFkjk0PYNACkK5oPAPxXWCIZ433W2e5rKZgca62z+WCYfAAAYGzTZ0lZ2Z5Htin+q5ecYdPZOVLFrLi/PjKTycpW4Lx3KfCNH8ocfdKhH2Ct7JMPDa9ieuKB4ewApBzX5IOpZeUSAKQzmg8AfGeCQamoxPPMhph8SCldISnsyOmg+QAAwJhMMCjNqvY+9CP3wdV8qKwZrg2IIzN1ugIf/5ICn71BmlF16Ad0d8r++keK/McXZbesj3+BiBkbCUs7t3sf1hI2DQDpjOYDgMRw5T6wdim1uFYuSTQfAACIgiv3wRUEHUu2cYfn7abakUUBxIFZskKB638gc+lVUk7OoR+w/XVFvv0vCn/vK7IbXx2exEVyGytsmuYDAKS1rEQXACBDuZoPoXZfy8AkuZoPJiAVl/laCgAAKanS8Ua/D2uXnJMP5D3AZyY7W+b898iuPE2R394irX7u0A/atFaRTWul+YsVeMdl0tKjZFyrXZFQY4ZN8/UGANIakw8AEsKUlHkfkPmQUmx7i/dBaRnrGgAAiIKpdIROtzTL9vbE7XVtqM05cWqq6uL2usBYTHmFgtd+WYF/vF6aPjO6B23ZoMh/3aDIv/+z7JrnmYRIRjsImwaATEXzAUBiOCYfyHxIMR2OyYdSVi4BABCVKkfzQYrv9INr6kHiSmQknDniaAVuuFnm4iuGA9CjsWOzIj/8N0W+8U+yq54mmDqJ2PqtnrezcgkA0h/NBwCJQeZDemhzNB/IewAAIDpTp0u5+Z5HNo7NB+tqPpROkXF9nwb4yGTnKHDB5QrccLN05HHRP3DXdkV+8m1Fvv5pRZ5/fDjsGAljI2GpYZv3Ic0HAEh7NB8AJEYJzYd0YB2TD4bmAwAAUTGBgFQ52/swnqHTruYDK5eQZMz0mQp+6qsKfO5GaeER0T9wd4PsLd9X5LpPKvL0I7JDQ/ErEm57GqX+Ps8jUzvP52IAAH6j+QAgIYwrjLiznT2tqcQVOE3zAQCAqJkq79DpuE4+OBobhpVLSFJm8ZEK/vO/KfCFb0lLVkT/wOYm2V/+lyJf/bgiTzwgOzgYvyIximvlkoJBVrwBQAag+QAgMVzj/AMDzitjkISczYdyf+sAACCVuXIf4jT5YMNhd56EoxECJAtz2FIFP3uDAv/6XWnZsdE/sKVZ9tc/UuQr1yjy6P2yA/3xKxJvqneETc8ibBoAMgHNBwCJUVLmPmP1Ukqw4bAUavc8Y+0SAADRM5WON/xD7bLx+L5ob6PkWEHD5ANShZm7UMFPX6fAdTdJR50Q/QPb9sve8TNFvvwxRR76gywXPsWVdTQfWLkEAJmB5gOAxHBlPkjON7SRZELtko14n9F8AAAgemNNG8Rh9ZIzbDoQkGY58ieAJGVq5in4iX9V4Ov/LXPsKZIx0T2wo032d79Q5F8+osiffyfb2xPXOjORjYSlnY6w6TrCpgEgE9B8AJAYuflSdo73WWe7r6VgglwrlySplLVLAABEraRMKir2PHJlM0yKq/kws1omOzv2rwf4wFTVKvCxLyjwjR/KnHDGcDMtGl0h2T/8ergJ8ac7ZLu74ltoJtnTKDnWW5lamg8AkAloPgBICGOMM/fBhli7lBI6Wrxvz8pyvoECAABGM8ZIrtVLjXGYfCBsGmnMzKxW4EOfVeCbP5E55RwpmBXdA3u6ZO+7Q5F//Ygif/i1bGcovoVmALvDkfdA2DQAZAyaDwASxxU6TeZDSrCuyYfSqcNvogAAgKgZR+i0jcPaJefkA2HTSCNm+kwFrv6UAv/2U5nTz5eyopzq6e2R/fPvhpsQv/uFbEdbXOtMazu3et9eWSPjmoIHAKQVmg8AEscVOk3zITW4mg/kPQAAMH6uyYemellrY/YytqdLat3necbkA9KRKZ+uwJUfV+BbP5M562IpJ8o3vfv7ZB/6gyL/+lFF7vy5bJtj6hdO7rBpVi4BQKag+QAgYYxr8oHA6dTQ7vgBjOYDAADjZlxTBz3dY+csjdeuMTIkaD4gjZmycgXe+2EFvnWLzHnvGs6gi8bggOwj9yny5Y8qctuPZFua41tomhgzbLp2nr/FAAAShuYDgMRxZT4w+ZASXGuXTBlh0wAAjFul99olSVIMQ6dt4w7vg4JCacq0mL0OkKxMSZkC7/qAAv/xc5kL3ivlF0b3wKEh2ccfUOQr1yjyyx/INjfFt9BUt3ussOkFPhcDAEgUmg8AEqeEzIeUNkbmAwAAGB9TWCQ5Gvi2KXbNB+fkQ3UdmU3IKKaoRIGLrxxuQlx8pVRYHN0Dw2HZpx9W5KvXKnLL92V3N8S30BTlWrk0HDZNvgwAZIqsRBdwKF1dXfriF7+o/fv369prr9Xpp58+7udobW3Vgw8+qDVr1mjPnj2KRCKqqKjQypUrdcEFF6igoCD2hU/Atm3b9NBDD2n9+vVqaWlRTk6OZs+erdNOO01nnHGGAgF6RUgzxWXet7N2KTV0OJoPU2g+AAAwIZU13msNG2MXOu2afDBVdTF7DSCVmIIimQveK3vWhbKP/UX2oT9GdzGUjcg+/7jsC0/IHHWizAWXyVTPiXu9KcPVfKiqJWwaADJI0jcffvSjH2n//v0TfvyTTz6pn//85+rr6zvo9oaGBjU0NOiRRx7RF77wBc2fn7jAo0gkottuu03/93//d1CY3ODgoDZu3KiNGzfq0Ucf1Ze+9CWVlJQkrE4g1kxJmTzjE7s6ZSNhmUDQ75IQJTs4KHV1ep4ZJh8AAJgQU1Uju371qNttjNYu2UhkjMkHrkRGZjN5BTLnvUv2jAtkn3xA9oE/uC+2OZC1squell31tLR8pQIXvJdAZRE2DQAYltSX0v/mN7/RSy+9NOHHP/vss7r55ptHNR4O1NbWphtvvFH19TEcZR6nX/7yl7r//vsPajy81ebNm3XDDTeM+WsBUo4rcNpGnG9sI0m4wqYl58oIAABwCK7Q6d07hxsHk9XSLPX3eh4x+QAMM7m5Cpx1sQLf+pnMFR+Xpo4jC2XN84p883MK/9cNsls3xq/IJGfDYalhu/dhDWHTAJBJknby4f7779e999474ce//vrruvnmmw96Q//MM8/Uueeeq2nTpmnnzp36zW9+o82bN6u3t1f/7//9P337299WTo6/43/333+/HnjggZHPc3Nz9a53vUsnnnii8vLytHbtWv3v//6v2tra1NDQoFtuuUWf+tSnfK0RiBtX5oM0POpcUuZbKRinsa4CK2PyAQCAiTCVtd5ToQMD0v69UsWsyb3Arh3uM1fjA8hQJjtH5ozzZU85W/bZv8n+5W5p357oHvzaKkVeWyUtPlKBd7xXZuHh8S022ezZ5Q6brmPyAQAySVJOPtx999363//930k9xy9+8QsNDg6OfP7Rj35U11xzjerq6lRUVKQlS5bo+uuv15w5wzsZGxsb9ec//3lSrzle7e3t+u1vfzvyeV5enm644QZdcsklqqioUElJiU466STdcMMNys/PlyQ98cQT2rx5s691AnFTNMYaMXIfkpsrbDo3T8rL97cWAADSReVs91kMQqetq/kwfaYM/34DnkxWtgKnnKPAjT+W+Yd/kmZWRf/gDa8o8r0vK/ydf5Fdv3rMbQfpxB02nSUxZQUAGSWpmg+Dg4P66U9/qrvuukuSZIxRVtb4hzOee+45bd26deTz0047TWefffao++Xk5OgjH/nIyOf33XefhoaGJlD5xPz+979Xf/+bVwN86EMf0ty5c0fdb+bMmbr00ktHPr/nnnt8qQ+IN5OVLRUUeZ7ZaELekDDW1XwoK5cxxt9iAABIEyY3T5o+0/PMxiB02hU2req6ST83kO5MMKjAiW9T4IabZT72hfFNC21er8hN1yvynX+VjXZ6IpXtcIVN18hkZ/tbCwAgoZKm+dDR0aHrrrtOjzzyiCQpGAzq05/+tKZMmTLu5zpwjVEwGNQVV1zhvO+CBQs0b97wzsHOzk69/PLL4369iejv79djjz028vns2bN12mmnOe9/5plnKvuNf6TXrFmjUCgU7xIBf7hWL9F8SG7O5gMrlwAAmJTKGu/bYxE67QibJu8BiJ4JBBU49hQFvvZfCnziX6Wa0RcQOm1Zr8h/3yg7OBC/ApOA3bnV83bCpgEg8yRN82HXrl3atm2bJKmoqEhf+cpXdPLJJ4/7ebq7u7Vhw4aRz1esWHHIBsZZZ5018vErr7wy7teciLVr1x409XDGGWeMebVwcXGxVq5cKUkKh8N67bXX4l4j4AtX6HSI5kNScwROm1KaDwAATIZxXE1tmyY3+WD7+6XmJu/XZPIBGDcTCMgcdYICX71JgU9fJ805LLoH7m6QffAP8S0ugYbDprd5H9J8AICMkzTNh79buHChvvOd7+jwwycWyLR9+/aD9igeddRRh3zMggULRj5+/fXXJ/S643XgWigpujoPO+zNb2Y2bdoU85qAhCgu8769s93PKjBO7rVLNB8AAJgU1+TDnkbZyayIbdopufbN03wAJswYI7PsWAX+9bsKfPYGacGSQz7G/vl36bt+aXeDNOA92WFq5/lcDAAg0cYfqBAnOTk5uvLKK3XhhRcqEJh4T6Sp6eCreRYtWnTIx1RVVSk7O1uDg4NqampSJBKZVA3ROLDO0tJSVVZWHvIxdXV1Ix/v2rUrHmUBvjMlpfL6MZjMhyTXQfMBAIB4MFW1nt8bKTw0PLngak4cgt213fsgJ1eaPmNCzwngTcYYackKBZeskN30miL/91tpg2OzwuCAIr+9RcFPfdXfIn1g671XLhE2DQCZKWkmHxYsWKCLL7540m/6t7a++YZYMBiM6k39YDCoadOmSRoOvW5p8V4nEksH1jl79uyoHjNjxps/FOzZk6ZXSSDzOCcfaD4ktTECpwEAwCTMrJKCQc+jSYVOuzIjqmplAt6vB2BizMLDFfzcjQr8y3ekwmLvO73yguwrL/hbmB/qXWHTtYRNA0AGSprmQ6x0dXWNfDxjxoyomxlFRUUjHx/YGIiX7u7ukY9nzpwZ1WMKCwtHPm5ra4t5TUBCuAKnQ+2+loHo2b4eqa/X88ww+QAAwKSYrGypwnEBVdPEQ6ftrh3er+fImAAweWbeIpl3XuU8j9z5c9mBfud5KrKO5gMrlwAgMyXN2qVYGTpgD+qhgqYP5Pcb+4ODgyMfR1tnTk7OyHqooaEhdXZ2qrjYcRVFlDo6OhQKhaK+fyAQUFVV1UG3BYPBuK+pQmoKvuWqvbd+Lknhsqny3F7c2aGsrLT7EpUWIp0hRRxnWeXTZfhzAwCkoGi+b/GLra5TZHfDqNtNU8OEvj+y1irsmHwI1Mzley4gjuzpb9fgUw/LbvfIl9y/V+bBe5R1qbtBkUpsOKywo9EZnLtQQb7WADGTTN+3ILkZYxL6+mn3lT8SefMtsQOnGQ4lJydn5OOenp6Y1uTlwFDs8TQQcnNzRxoX3d3dk24+PPjgg7r77rujvn9xcbFuvfXWg26bPn36pGpA5igvH72Sp79mjpq97tzXq2klJQrk5cW9LoxP3+6d2uc4m75goQK5/JkBAFKf1/ctfulYuFShF58cdXtgzy5VVFSM+/nCLfvU1OV9wdHUI1YobwLPCSB6A5/5qvZ+9gOeoe/hP9+t6RdepuzK6NYxJ7OBHVu01zHJUb7iOOXwtQaIm0R+3wKMJe0uVz/wCvzc3NyoH3fg1T5+NB8OrPPAxsehHNjJ7O31XnsCpJJAmXvyJxJivVgyCrd6tx4CRSU0HgAAiIHsmrmetw/tblCkv2/czzewfbP7termj/v5AIxPzoIlKnz7u7wPhwbV/pPvHnSBYqoa2Lze+yArS9l1rF0CgEyUds2H7AkGGB34pv6Bq5viZaJ1HtgkOXB1E5CqgqXujICwK9QYCeVqPgTLmYICACAWnG/SWauhhh3jfr5Bxw72YHmFgiVl434+AONXevUnFHD8fetb9Yx6n33M13riYXDLBs/bs+sWyGRHf9ElACB9pN3apbwDVrSM5835cDjs+XG8HFjneJodB973wBVTE3XuuefqhBNOiPr+XtkO+/btS4urNBB7wWDwoNG/lpaWUX+/rLVSMCh5/L1rrd+uYBlvaCeboQbvndFDRaVqbvZcogUAQNKL5vsWv9hgjpSVLQ2N/nmmZe1qBUvcF294Gdyw1vt1Kmv4txvwUeDdH1Tkf/6f51nLj7+jztnzZFJ4knjA8bUmXFXH1xogxpLp+xYkN2NMQlfmp13z4cCch87Ozqgfd+B9/QjiOLDO8QQ+d3V1xbSO0tJSlZaWTuo5wuFwTBohSH/hcNi72VZcKnlMOYTbW2V9mETC+ERa93sflE7xZXIMAAA/OL9v8UvlbGnntlE3hxu2yQ6dNq6nijRs97zdVtbwbzfgI7vydOnxB6StG0cftu7T4L2/UeCdH/C9rliw4bCsx9csSbI1c/haA8RZwr9vQdLyupDc19dP6KvHQVlZ2cjHLS0tUT/uwDf1x5PBMFEHvuHf2hrdapne3t6DupjjybQAklqxowEW6vC3DkTFdji+ZpWN7ypMAADgZiprPW+3jTvH9Tx2aFDavcv7sLpunFUBmAwTCChwxccl4/1WjH3oXlnX39dkt3unNDjgeWRqF/hcDAAgWaRd82HGjBkjH+/du1cDA97/+L3V/v1vXslbUFAQ87reaubMmSMfNzQ0RPWYA2uU/KkT8IVr13Bnu59VIFquLA6aDwAAxE5VjfftTd7rD532NEph7yshDc0HwHemZq7M297hfRgeUuSOn6bkWmNbv9X7ICvL/fUMAJD20q75UFtbOxIeHYlEtGWLd7jagZqamg5auzRt2rS41fd3c+bMGfl4+/btUTVJNm3aNPKxMeag3W5AKjPFZd4HoXY/y0AUrLXO5oMp42sSAACxYqq8Jx/Uul+2pzvq57GNjmZFMEuaWTWBygBMlrnoCvcFWBtekX3pKV/riYkdjvdequpksrL9rQUAkDTSrvmQn5+vefPmjXy+Zs2aQz7mwDf1JamysjLWZY2yePHikSbJwMCA1q9ff8jHbNz45l7I6dOnKysr7SI7kKlKvNcu2U7WLiWd7k7P8EtJTD4AABBLjrVLkqSmcaxe2rXD+/ZZ1bwhCCSIKSiUec8/OM/tXbfK9vX4WNHk2Xrv5oOpne9zJQCAZJJ2zQdJWrZs2cjHjz/++CHT3letWjXy8dSpU32ZfMjLy9Nhhx028vmjjz465v2Hhob0yiuvjHx+4GOBlEfmQ+pwrVySpFKaDwAAxMzUaVJevueRHcfqJetoPjgnKwD4wqw8XTpsqfdhe6vsfXf6Ws9k2KEhd6Ozdp737QCAjJCWzYfTTjtNxhhJUltbm/76178677t79+6Dmg9HHnlk3Ov7u9NPP33k4xdeeEE7duxw3vepp55SR8ebb8QuX748foUBfnOtXWLyIfm4mg/GuEfHAQDAuBljJFeDoCm6zDhJ7jcEyXsAEsoYMxw+HXCETz/8J/fatGSzu2GMsGkmHwAgk6Vl82HGjBkHTT/ccccdnm/sDw4O6sc//vFBkxGnnXaaHyVKkk466SQVFhZKGs6nuPnmm9XdPXp/a3Nzs2677baRz/Py8nTcccf5VicQb8axdkldHbKRiL/FYEy2w9F8KC6VYRUcAAAxZSq9Q1qjfUPSdoWk9hbv56b5ACScqaqVOesi78NIRJHbf5IS4dOulUuETQMA0rL5IEnvete7RqYfent7dcMNN+iJJ57Q0NCQJGnnzp268cYbD8pRWLx4sZYsWeJ8zssuu2zkv69//euTrjEnJ0cXXnjhyOc7d+7U1772tZGaIpGIXn75ZV1//fUKhUIj93vHO96hvLy8Sb8+kDRca5fCYak3+kBF+MA1+UDYNAAAseeafIj2auix7kfzAUgK5sLL3dlpr6+Tff4xX+uZEFfzgbBpAMh4aXuZ6qJFi3TxxRfrj3/8oySpu7tbN998s2699Vbl5OQctMJIknJzc/XhD3/Y9zovuugivfLKK9qwYYMkqaGhQV/72tdUXFyscDisnp6DQ6aqqqp08cUX+14nEFeutUvScO5DYbFvpeAQHFdPEjYNAEDsmcoaeV7z3NkhG2qXOcTKQ7vL0XwoKiarCUgSJq9A5rIPy/7su57n9ne/kF12rExBkc+VRc/Wb/W83dSxcgkAMl3aTj5I0vve9z5ddNHBI4y9vb2jGg85OTn6zGc+o5oa/8cBs7Ky9KUvfemgNVGS1NnZOarxMH36dH3pS19i6gHpx7V2SZI6230rA4dmHZMPhuYDAACxN1YodNPOQz++cYfjeetGpsQBJJ455mRpsSN/MtQu+6c7/C1oHOzQkNSw3fuQvAcAyHhp3Xwwxuj973+/vvzlL6uystLzPgsWLNCNN96oY445xufq3lRQUKAvf/nL+uAHP6iiotFXMxhjdPLJJ+tb3/qWZs6cmYAKgfgy2TlSfoH3IaHTycW1domrJwEAiDlTUiYVlXie2cZDNx+sI2yavAcguRhjFHjfNVLQezmFffT/ZHdu87mqKDXtlIYGPY9M7TyfiwEAJJukX7v0wx/+cNLPsXz5ct10001at26dNm7cqFAopNLSUi1btkwLFiyI+nnuuuuuSdfiEggEdP755+uss87SSy+9pO3bt2twcFAVFRU66qijaDog/RWXSr09o262oXZxXV4ScWY+0HwAACAuqmqlTWtH3940du6DjYTdmQ9jTVQASAgzq1rmnItl//L70Yd2OHw68MX/kAkk1zWk7rDpbKmSsGkAyHRJ33yIFWOMDj/8cB1++OGJLmVMOTk5OvHEE3XiiScmuhTAX8WlUvPu0beHmHxIFjYSlkJtnmdmCoHTAADEg6mskfVoPthDhU7v2ysN9Hs/Z/WcWJQGIMbMO94r+/zjUuv+0YdbN8o++6jMSWf5X9hYdnrnPaiasGkAQJqvXQKQQlyh02Q+JI9QhxSJeJ+xdgkAgPhwTSk07ZS1nnHUw1x5D8ZwNTKQpExungLv/Yjz3N79S9nuTh8rOjS7w3vygZVLAACJ5gOAJGEcodOWzIfk0eFYuSSxdgkAgDgxVY5GQW+P1OZxdfQbXHkPmj5LJjd38oUBiI8VJ0iHH+V91hWS/eNt/tYzBjs0JLm+1hA2DQAQzQcAyaLYu/nA2qUk4sp7CAadYZgAAGCSxppSGCN02tl8IGwaSGrD4dMfk7Ic4dOPPyC7Y7PPVTmMGTZN8wEAQPMBQLJwrl2i+ZAsrKv5UDol6YLvAABIF6agSJoyzfPMjhU67Wg+GJoPQNIzFZUy573L+9BaRX7zk+E8tgQjbBoAcCi8WwQgOTjWLpH5kERczYcywqYBAIgr1+olR+i07euV9u3xPKP5AKQG8/Z3S9NmeB/u2Cz71F/9LciLq/kwe46MY3IDAJBZaD4ASArGtXapp1vWMcoLn7W3eN9O3gMAAHFlKr1Dp61r7VKTex0Ta5eA1GBychW4/GPOc3vPr2U7Qz5W5FFD/VbP2wmbBgD8Hc0HAMmhpMx9luBvqjHMtXbJlNJ8AAAgrlyTD3saPFevOPMecvOl8orY1QUgrsyRx0pHHud92N0pe8+v/C3oAHZoUNq13fuwhuYDAGAYzQcAycGV+SCxeilZONcu0XwAACCeTJX35IMGBqT9e0ff7mo+VNWQ0wSkmMB7PyJl53ie2af+Krt1o88VvaFppzQ05Hlk6hb4XAwAIFnxnSeA5FBYJBnHl6RQu6+lwKGD5gMAAAkxc7ZkjPeZx+ol27jD867kPQCpx0yfKXP+e5znkdsTEz7tWrmkrGxp1mx/iwEAJC2aDwCSggkEpOISzzMb6vC5GryVHRqUOr3/HAyB0wAAxJXJzZWmz/Q8s28JnbbWuicfaD4AKcmce6lUMcv7cOc22cf+4m9BkrSDsGkAwKHRfACQPFyh0443veGjjjb3GZMPAADEnyN0elS4dFuL1NPteVdTVRfbmgD4wmTnKPC+McKn//gb2dAY36/Hga33bj6Y2vm+1gEASG40HwAkD1foNJkPiefKe5BoPgAA4ANT6R06/dbJBzlWLkmSqh0NDABJzxx+tHTUCd6Hvd2yd//St1rs0KD7a00tYdMAgDfRfACQNIxr8oG1S4nnaj7k5Ej5hf7WAgBAJqrybj5ob+PwG4FvsK6VS1OnyRQUxb4uAL4JvPcjUk6u55l99m+yr6/zp5DGMcKmmXwAAByA5gOA5OFoPljWLiWcdTUfSqfKuAIwAQBAzJgqx9RCOCztbXrzc1fzgZVLQMozU6fLXHC58zxy+09kw/EPn3atXFJ2DmHTAICD0HwAkDzIfEheHS3et08hbBoAAF/MqJSCQc+jA1cvuSYfDGHTQFowZ18kzaz2Pmysl330/vgX4Wo+VNcRNg0AOAjNBwDJg8yH5NXmPflgSsl7AADADyYrW5pR5X3YOBw6bQcHpT27vO9D8wFICyYrW4ErrnGe2z/dLtvuuHAoRmz9Vs/bWbkEAHgrmg8AkoZxNR9CHbLW+loLDmY7HGuXCJsGAMA3rtVLtumNyYc9u6RIxPuxNB+AtGEWHylz7Cneh329sr/7Rdxe2w4Oute71dF8AAAcjOYDgOThWrs0NCj19vhbCw7mynyg+QAAgH8qHaHTb6xdcoZNZ2VJFZXxqQlAQpj3fEjKzfc8sy88Ibvhlfi8cFO9FHaFTc+Lz2sCAFIWzQcAycPVfJDIfUi0MQKnAQCAP5yh0/v3yvb3u69GnjW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S7KfnAwAAAAAUg/ABqAxZTaf1fMju+WDbJQAAAACKRPgAVIasptNVHj6ku3dF7NiWdyyx8gEAAACAIhE+ABUhyVr5UO0Np7duyR4TPgAAAABQJMIHoDJk9Xyo8pUPmVsuRQgfAAAAACga4QNQGZr0fMgnzWo2PXhIJIOHlrYYAAAAAKqG8AGoDFk9H6p926WslQ9WPQAAAABQRMIHoCIkjSPyD2xri7Srs6S1DChZ4UOz8AEAAACA4hE+AJUhq+F02hWxra20tQwkGeFDYuUDAAAAAEVU198FFNsdd9wRd95550G95tJLL403vvGNRaoo29q1a+Pee++NxYsXx4YNGyJJkpgwYUKcc845ceGFF0Z9fX3Ja4KykdXzIWJv34esbZkqXLo1a9ul0aUtBAAAAICqUvHhw4oVK/q7hD65++674/bbb4/Ozp7bw6xYsSJWrFgR//mf/xmf+MQnYvz48f1UIQxww5uyx1pbIiZOLlkpA8qWjIbTVj4AAAAAUEQVv+1SOYQPP/3pT+OWW27pFTx0t2bNmrjuuuti8+aMbzFDlUvq6iOGDs87lrZtLXE1A0OaphEZKx9suwQAAABAMVX0yofNmzdHS0tLREQMGTIkvvvd7/bpdXV1pftrmT9/ftxyyy2585qamrjooovita99bTQ1NcWSJUviBz/4QaxZsya2bNkSX/va1+Laa6+NJElKViOUjabmiB3bel+v0vAhdrZH7NqZf0z4AAAAAEARVXT40H3Vw6RJk6KhoaEfq+lt9+7d8a//+q97v50cEbW1tfGxj30sTjvttNw9p512WkybNi2uvvrq2LRpUzz11FPxu9/9Ls4+++z+KhsGrsbmiHUv9b7eWqXhQ0az6YiIaBY+AAAAAFA8Fb3tUvfw4eijj+7HSvK79957Y9OmP+zH/va3v71H8LBPY2Nj/Pmf/3nu/K677ipJfVB2Gkfkv97WUsoqBo6WjH4PERpOAwAAAFBUVRM+HHvssf1YSX733Xdf7rixsTHe9ra3Zd77qle9KkaOHBkREatWrYrnn3++yNVB+UmamvNer9qeD1krH4Y3RlJfX9piAAAAAKgqwod+smrVqtiwYUPu/Nxzz93vtlC1tbXx2te+Nne+cOHCYpYH5Slz5UN1hg+Z2y7ZcgkAAACAIqvY8KF7s+m6uro45phj+rWeV1q2bFmP83zbLb3StGnTcsdLliwpeE1Q9jJWPkRrS0nLGDC2ZoQPmk0DAAAAUGQVGz50/3B/ypQpUT/AthhZs2ZN7rimpiZOOOGEA76me4CyevXqYpQFZS3JWvlQpQ2n04yeD4nwAQAAAIAiq+vvAorllc2mf/nLX8aCBQvipZdeio0bN0ZdXV2MHj06TjrppDj77LPjpJNOKml9mzf/4RvJY8aMicGDBx/wNaNHj466urro6OiIDRs2RFdXV9TUVGx+BAevMWPlw672SHftimTQoNLW09+ytl3SbBoAAACAIqvY8GH58uW541/84hfxi1/8osf4rl27Yvv27bFq1aq477774vjjj4+PfOQjMWHChJLUt3379tzx+PHj+/y6YcOGxdatW6OzszNaWlpi1CjfYIacrG2XIiK2bY0YNLZ0tQwEmeGDnxsAAAAAFFdVhA/7JEkSI0aMiBEjRsTmzZtj69Y/bMWyZMmSuOaaa+LTn/50TJw4sej1dXR05I5HjhzZ59cNHz48V3chwoetW7dGa2trn++vqanp9fdTW1trBQZ51dbW7ve80NKRo2N3Vi3bt0XNuNKEiwNBmqbRmRE+1I4aE7V1FfvjHwAOSannLQAAh8q8hb5KkqRf378iP31at25dbNu2LXc+bNiweOc73xlz586NxsbG3PU1a9bEHXfcEb/97W8jIqKtrS2+8Y1vxGc/+9mi19jV1ZU7Hj58eJ9f19DQkDvuvnriUN13331x55139vn+xsbG+P73v9/j2pgxYw67DqrD6NHF3e4nTcfE6traiM7OXmPNNRFDxlbPyofOrS2xprMj79ioKVNjUBX9XQDAoSj2vAUAoFDMWxioKvLr6s8991zuePTo0fH5z38+3vKWt/QIHiIiJkyYEH/zN38Tb37zm3PXli5dGo8//njRa+y+UmDQQexDX9ft28rt7e0FrQnKXZIkUZuxpVDn1owtiCpU5+YNmWO1owSGAAAAABRXRa58OPvss2PatGmxcePGGDNmzAF7Krz3ve+NpUuXxpIlSyIi4j//8z9j9uzZRa2xvr7+kF7XfRnVnj17ClUOVIya5pHRuan3B+9dWf0PKlS+v4OIiKipidqRej4AAAAAUFwVGT7U19fHhAkT+tw8OkmSeNOb3pQLHxYuXBgdHR09VhkU2uDBg3PHBxMidHbbTqYzz9YyB+uCCy6IV7/61X2+P19vhw0bNkSapoddC5Wntra2x9K/TZs2FeT/t/vTMbQx7/Vta1+KnevXF/W9B5LO53v3vYmIiKYRsWFTdQUxANAX/TFvAQA4FOYt9FWSJP26ZX5Fhg+H4qSTTsod7969O1avXh3HHHNM0d6ve5+Htra2Pr+u+72FaBjS3Nwczc3Nh/WMzs7OHj0sIEtnZ2ePZutFMbwp7+Wuls3Ff+8BpCtr26XmUVX19wAAh6ok8xYAgAIwbyFLvi+Sl/T9+/XdB5BX9oPYuHFjUd9vxIgRuePNm/v+LeTujbQPplcEVI2m/GFa2ra1xIX0s6xtpjJ6YgAAAABAIQkf/tuOHTt6nO/cubOo7zdu3Ljc8apVq/r0mh07dsT27dtz50OHDi14XVD2GjNW8rRWV/iQZoQPSbPwAQAAAIDiq+jw4WB6Kbxy9cGQIUMKXU4Pxx57bO54y5YtfVppsXTp0h69FY444oii1AZlrXFE/utWPuyl2TQAAAAAJVBx4cOWLVvilltuiY997GNx7bXX9vl1ixcv7nFe7A/2J02aFCNHjsydP/744wd8zXPPPZc7rq+vFz5AHknGtkuxbWuk1dSbJCt8sPIBAAAAgBKouPAhIuLuu++OF154IZYvXx7r16/v02t++9vf5o4bGxvjqKOOKlZ5OdOnT88dP/DAAwe8/9FHH80dT5kyJerq9AuHXrK2XersjGjfnn+swqSdnRGtLXnHkhGjS1sMAAAAAFWp4sKHkSNHxuTJk3Pnd9111wFf86tf/SqWL1+eO58zZ05JOoHPmzcvd7x8+fJ47LHHMu996qmnYuXKlbnzmTNnFrM0KF9Z2y5FVE/fh7aWiDRjlYeG0wAAAACUQMWFDxERb3jDG3LH//Vf/7XfLY0WLlwYN998c+48SZL4oz/6o6LWt8+pp54aEyZMyJ1/5zvfydv7Ydu2bfHd7363R42vec1rSlIjlJ2sbZci9n4oXw2ytlyKED4AAAAAUBIVGT7MmzcvjjzyyIiISNM0vvjFL8add94Z69evj67/3vN97dq1cdttt8UXvvCFaG9vz732kksu6REIdHfZZZfFJZdcEpdccklcdtllBan14osvzh1v3rw5rrnmmnjsscdydT777LNx3XXXxZo1a3L3nXfeeTF27NiCvD9UmqS+IWLI0PyD1dJ0Oit8qK2LGN5U2loAAAAAqEoV2TSgoaEhLr/88vjMZz4T7e3t0dHREXfccUfccccdUVtbG2ma5j7c7+5Nb3pTvPOd7yxpreeee24sWLAg13Ni06ZN8U//9E8xbNiwqKmpiba2th73Nzc3x7vf/e6S1ghlp7E5on1Hr8tpa0sk/VBOqaUtm/IPjBgVSVINfwMAAAAA9LeKXPkQETF16tS47rrreq1i6Ozs7BU8TJw4Ma6++up4//vfX8IK/+CjH/1onHvuuT2ubd++vVfw0NjYGFdffXWMGmXbFNivrKbT1dLzIWvlgy2XAAAAACiRilz5sM+UKVPiS1/6Uvz617+Ohx56KFasWBE7d+6M4cOHx4gRI+LEE0+M2bNnx6xZs0rSYDpLXV1dXHHFFTF79uy49dZbY/Pm3h8czp49Oz7wgQ/EuHHj+qFCKDNZTaervedDs/ABAAAAgNKo6PAhYu8H++eff36cf/75h/2sm266qQAVZTvvvPPi7LPPjoULF8bSpUujvb09Ro0aFbNnz46jjz66qO8NlSRpao40z/W0Sno+pFvzhw+JlQ8AAAAAlEjFhw/lpra2Nk4//fQ4/fTT+7sUKF+2Xcp/fcTo0tYBAAAAQNWq2J4PQBXL3HapWsKH7IbTAAAAAFAKwgeg8jRlrHyogp4P6Z49Edva8o7ZdgkAAACAUhE+ABUnydp2acf2SDv2lLaYUsvo9xARVj4AAAAAUDLCB6DyNI3IHmtrLVkZ/SKr30NERLPwAQAAAIDSED4AlSer50NERGtLqaroH1krHwYNjhgytLS1AAAAAFC1hA9A5Rk2PCLJ+PFW4X0f0i0ZzaabR0WSJKUtBgAAAICqJXwAKk5SUxPR2JR3LG3dWuJqSixr2yX9HgAAAAAoIeEDUJmymk63VXj4kLHtUiJ8AAAAAKCEhA9AZcpqOl3p2y5Z+QAAAADAACB8ACpSkrXyoWq3XRpd2joAAAAAqGrCB6AyZYQPaaVvu9SS0XDaygcAAAAASkj4AFSmKuz5kO7cEbGzPe9Y0ix8AAAAAKB0hA9AZarGng8tW7LHrHwAAAAAoISED0BFSrLCh9aWSNO0pLWUzNaMfg8RwgcAAAAASkr4AFSmrG2XOjoi2neUtpYSSbOaTQ8dHknDoNIWAwAAAEBVEz4AlSkrfIio3L4Pmk0DAAAAMEAIH4DKlLXtUkTl9n3IWvkgfAAAAACgxIQPQEVKBg2OyNpqqLVSVz7kDx+SZuEDAAAAAKUlfAAqV8bWS2mFbruU2fPBygcAAAAASkz4AFSurK2XKnXbpa0Z4cPI0aWtAwAAAICqJ3wAKldW0+kK3HYpbW2J2Phy3rGkeWRpiwEAAACg6gkfgIqVZK58qMDwYfGCiDTNPzjpmJLWAgAAAADCB6ByZfV8aG0pbR0lkC58OP/AuImRjJ1Q2mIAAAAAqHrCB6ByNWVsu1RhKx/SPbsjnn4871gy88wSVwMAAAAAwgegkjWOyH+90hpOP/tExO5deYeSmXNKXAwAAAAACB+ACpZkNZze1hZpZ2dpiymizC2XhjVGHHdSaYsBAAAAgBA+AJUsa9uliIhtraWro4jSrq5In8gfPiTTz4iktrbEFQEAAACA8AGoZFnbLkVUztZLq5ZHtGzOO5TM0u8BAAAAgP4hfAAq1/Cm7LHWymg6nS56JP9AbV3EybNLWwwAAAAA/DfhA1CxktraiOGNecfStkoJH36ff+CE6ZEMGVraYgAAAADgvwkfgMqWtfVSBWy7lG7aEPHiyrxjtlwCAAAAoD8JH4DK1jQi//UK2HYpq9F0REQyQ/gAAAAAQP8RPgAVLWlszj/Q2lLSOoohXZQRPkw6NpLRY0pbDAAAAAB0I3wAKltG+FDuPR/S9h0Rzy7OO2bLJQAAAAD6m/ABqGxNGSsfyjx8iKcfj+jsyDuUzBQ+AAAAANC/hA9AZctqOF3m2y6lCzO2XBoxKuLo40pbDAAAAAC8gvABqGiZPR/KeOVD2tkZ6eIFeceSGWdGUuNHOwAAAAD9yydUQGXL2nZp965Id+0sbS2FsvzZiO1teYeSmXNKXAwAAAAA9CZ8ACpb1rZLEWW79VK6KGPLpYZBESfOKG0xAAAAAJCH8AGobFnbLkWU7dZLmeHDybMjaRhU2mIAAAAAIA/hA1DZhgyNqKvPP1aG4UO6bnXEyy/lHUtmnVniagAAAAAgP+EDUNGSJMns+5CW4bZL6aJH8g8kSSTTzyhtMQAAAACQQfgAVL6svg9lGT78Pv/AlBMiaRpR0loAAAAAIIvwAah8WX0fymzbpbStNWLZs3nHkpm2XAIAAABg4BA+ABUvqZTwYfGCiLQr75jwAQAAAICBRPgAVL6sng/lFj488XD+gTHjI448qrTFAAAAAMB+CB+AylcBPR/SPXsinnw871gy88y9jbUBAAAAYIAQPgCVrxK2XXpuccSu9rxDtlwCAAAAYKARPgAVL8nYdinaWiPtyt9DYaBJF2VsuTR0WMTUk0tbDAAAAAAcgPABqHxNI/JfT7sitm8raSmHIk3TzH4PyalnRFJXV+KKAAAAAGD/hA9A5cvq+RBRHn0fXlwZsXlj/rGZc0pbCwAAAAD0gfABqHyNTdljbS0lK+NQZW65VFsbyamnlbYYAAAAAOgD4QNQ8ZK6+r29EfJIy6DpdGb4cPypkQwdXtpiAAAAAKAPhA9Adcjaeql1YIcP6ZZNES8syzuWzDyzxNUAAAAAQN8IH4Dq0Nic//oA33YpfeKRzLFkhn4PAAAAAAxMwgegOjRlhQ8DfOVD1pZLEydHMmZ8aYsBAAAAgD4SPgBVIclY+ZAO4G2X0p3tEc8syjtmyyUAAAAABjLhA1Adsno+DORtl55eGNGxJ++Q8AEAAACAgUz4AFSHphH5rw/gbZfSJzK2XGoaEXHMtJLWAgAAAAAHQ/gAVIUkq+dDa0tJ6+irtKsz0icW5B1LZsyJpMaPbwAAAAAGLp9eAdUho+dD7GyPdPeu0tbSFyuWZK7KsOUSAAAAAAOd8AGoDlk9HyIi2lpLVkZfpYsytlyqb4g4aVZJawEAAACAgyV8AKpD1rZLEQOy6XRm+HDyrEgGDSptMQAAAABwkIQPQHUYMiyitjb/2ABrOp2uXxOx9sW8Y8mMOSWuBgAAAAAOnvABqApJTU3E8PyrH9LWARY+LHokc0z4AAAAAEA5ED4A1SOr6fQA23Ypc8ulY4+PZMSo0hYDAAAAAIdA+ABUj6y+DwNo26V0+7aIpU/lHUtmnlniagAAAADg0AgfgKqRNI3IPzCAtl1Kn3w0oqsr71gy05ZLAAAAAJQH4QNQPTK2XUpbW0pbx/5kbbk0emzExGNKWgoAAAAAHCrhA1A9Gkfkvz5Aej6kHXv2rnzII5l5ZiRJUuKKAAAAAODQCB+A6jHQez4seSqifUfeIf0eAAAAACgnwgegaiQZ2y5F29ZI07S0xeSRPvFI/oEhQyOOP6W0xQAAAADAYRA+ANUja9ulzs6IHdtLWsorpWka6cLf5x1LTjktkrr6ElcEAAAAAIdO+ABUj6xtlyL6v+/DSy9EbFqff8yWSwAAAACUGeEDUD2G7yd8aO3fvg/poofzD9TURDL99NIWAwAAAACHSfgAVI1k0KCIQUPyD/Zz0+nM8GHaKZEMayxtMQAAAABwmIQPQHXJ2Hop7cdtl9KtWyJWLsk7lsyYU+JqAAAAAODwCR+A6tI0Iv/11pZSVtFD+sQjmWOJfg8AAAAAlCHhA1BdGjP6PvTjtkuZWy4deVQk4yaUthgAAAAAKICqCx/SNI3du3dHV1dXf5cC9IMkI3xI+6nhdLprV8TTC/OOWfUAAAAAQLmq6+8Ciq2zszMeeuih+M1vfhMrV66Mtra2SNM0ampqYty4cTFnzpx4y1veEiNGjOiX+q677rp45plnDuo1X/nKV2LixIlFqggqXOOI/Nf7q+fDs4si9uzOO5TM1O8BAAAAgPJU0eHDyy+/HF/5yldixYoVvca6urpi7dq1cffdd8f9998ff/VXfxVnn312SetL0zSef/75kr4nVL2MhtP9te1S5pZLw5sippxQ2mIAAAAAoEAqNnzYsGFDXH/99bFp06YD3rtz587453/+50jTNM4555wSVLfX2rVro729vWTvB0R2z4d+2HYp7erKDB+SGXMiqaktcUUAAAAAUBgVGT50dXXFV7/61R7Bw6RJk+LNb35zTJ8+PYYOHRovvvhi3HXXXbFo0aKI2LsK4eabb47p06dHU1NTSersviLjtNNOiyuvvLJPr6uvry9WSVDxksbmSPMN7NgWaceeSOpK+O/X80sjWlvyDun3AAAAAEA5q8iG0//1X/8Vzz33XO78LW95S9xwww3xute9LsaOHRvDhw+Pk046Kf7+7/8+5s2bl7uvra0t7rnnnpLV2T18OProo6OhoaFP/yRJUrIaoeI0jcge29ZasjIiItJFj+QfqKuPOHlWSWsBAAAAgEKquPChs7MzfvSjH+XOL7zwwnjve98bdXW9F3kkSRKXXnppj5UOCxYsKEmdEb3DB6AE9hc+lHjrpXTR7/MPnDgjksFDSloLAAAAABRSxYUPjz/+eGzcuDEiIkaPHh3vec979nt/Q0NDzJo1K3e+Zs2aYpaXk6ZprFy5Mnd+zDHHlOR9oeoNGx6RZPzoK2HT6XTDuoiXXsg7ZsslAAAAAMpdxfV8qKuri3nz5sWLL74Yc+fOjYaGhgO+ZuTIkbnjzs7O6OzsjNra4jZ6XbNmTa7Z9KBBg2LChAlFfT9gr6SmNmJ4Y96gIW1tiVJtapY+kbHlUuxtNg0AAAAA5aziwodZs2b1WMnQF1u3/uFDyIaGhqIHDxE9t1w67rjjoqam4hahwMDVNCL/Koe2lpKVkC56OP/A5KmRjDqiZHUAAAAAQDH4xDuiR3Pqo446qiTvuXz58tzx8ccfX5L3BP5bY3P+6yXq+ZDu2B6x5Mm8Y1Y9AAAAAFAJKm7lw8FauXJlrF27Nnd+sKsmDlX3lQ9HHHFE/PSnP42FCxfGyy+/HFu2bIlhw4bFmDFjYtasWXHOOefYlgkKKGlsjjTfQIl6PqRPPRbR2Zl3LJml3wMAAAAA5a/qw4ef/exnueMkSeK8884r+nt2dXX1aDb9ve99r9c9LS0t0dLSEkuXLo0777wzXvOa18Rf/MVfxNChQ4teH1S8phF5L6elaji9MGPLpZFHRBw1pTQ1AAAAAEARVXX4sGrVqnjooYdy53PmzCnJCoPVq1fHrl27el2vra2N0aNHx9ChQ+Pll1/ONaRO0zR+9atfxerVq+NTn/pUn5po99XWrVujtbW1z/fX1NTExIkTe9WtZwX5vLJ/Sin6qfRFR/PIyLfuIGnbGnV1xf2xmHZ0ROeTj+Ydq5n9qqivry/q+wMA+Q3UeQsAwCuZt9BXSZL06/tXbfiQpml8//vfj66urojY+y/pu971rpK899KlS3ucT5gwId71rnfF6aef3uODx8WLF8dtt92W6w+xfPnyuPPOO+Pd7353wWq577774s477+zz/Y2NjfH973+/x7UxY8YUrB4q2+jRo/u7hIiI2DbxqNiS53rN9rYYO3ZsUd975xMLYsOObXnHRs29IIYU+f0BgL4ZKPMWAIADMW9hoKrar6vff//98cwzz+TOL7zwwpg0aVJJ3rt7g+vp06fHP/3TP8VZZ53V6xvP06dPj+uvvz5OPPHE3LX/+I//OKiVCkBvtc0j817v2rol0jRvN4iCaf/9r/NeT4YMjcEzzyjqewMAAABAqVTlyod169bFLbfckjsfN25c/Mmf/EnJ3v/9739/vPnNb44NGzbEySefHIMHD868d9CgQXHVVVfFFVdcEe3t7bFr16546KGH4n/8j/9Rsnqh0tSMzP+NgHT3rkjbd0QydFhR3jdN09iZET4MPu2sSOoLt6UaAAAAAPSnqgsfOjo64sYbb8z1XKipqYmPfvSj+w0ACm3o0KExefLkmDx5cp/ub25ujvPOOy/uv//+iIhYsGBBwcKHCy64IF796lf3+f58vR02bNhQ9G+LU5729THZZ9OmTdHZma/bQmmlHV2ZYxuWL41kXHF6v3S9tCo61q7OO7bnpNmxfv36orwvAHBgA3XeAgDwSuYt9FWSJP26ZX7VhQ+33nprrFixInd+8cUXxwknnNCPFfXNiSeemAsfutd/uJqbm6O5ufmwntHZ2ZnrnQH709nZGR0dHf1dRqRDh2eOdWzZFMno4vRd6Hrsd/kHkproOnl2pAPg7wYA2GugzFsAAA7EvIUs+b5IXtL379d3L7H58+fHPffckzufMWNGvOMd7+jHivquqakpd7x9+/Zob2/vx2qgzA0aHNGQscVR29aivW266Pf5B447MZLGpvxjAAAAAFCGqiZ8WL16dXzzm9/MnY8ePTquuOKKfk9/+mr79u09zoUPcOiSJIloHJF3LG1rKcp7pq0tESueyzuWzDqzKO8JAAAAAP2lPD55P0zbtm2LG264IfeBfX19fVx11VU9VhP0hz179vT53s2bN/c4Hzp0aKHLgerSmLHdWGtxVj6kixdEZPRGSWYKHwAAAACoLBXf86GjoyO+/OUvx7p163LX/vIv/zKmTp3aL/UsX748fv7zn8eiRYvi1a9+dbz3ve/t0+sWL16cOx42bFhJG2RDRcoKH4q07VK68OH8A+MmRjJ+UlHeEwAAAAD6S0WvfOjq6opvfOMb8eSTT+auXXjhhXH++ef3W01r166NBx54IDZt2hS///3vI834JnR3ra2tPf4Mp5xySjFLhKqQNJUufEj37I54+vH8dcycU/D3AwAAAID+VrHhQ5qm8Z3vfCcefPDB3LVTTz013ve+9/VjVXubXNfW1kZExIYNG+I3v/nNAV9zyy23xO7du3PnZ55pixY4bFk9H1pbCv9ezz4RsXtX3iFbLgEAAABQiSo2fLj99tvjgQceyJ2PHz8+rrzyytwH//2lqampR3hw6623xvr16zPvv+uuu+JXv/pV7nzMmDFxzjnnFLVGqApNI/JfL8bKh6wtl4Y1Rhx3UsHfDwAAAAD6W0X2fFiyZEn85Cc/yZ0PGTIkrrzyymhoaOixgiBLXV1d1NT0zGUuu+yy2LBhQ0TsDQBuuummQ67vj//4j+ORRx6Jjo6O2LJlS1x77bVxySWXxJw5c6KxsTE6OztjxYoV8ZOf/CQefvgPH1rW1tbGhz/84X4PUKAiZDacbino26RpGukT+cOHZPoZkfj3GQAAAIAKVJHhw89+9rMevRTa29vj6quv7vPrP/KRj8S8efOKUNlekyZNive///3xve99LyIiNm/eHN/61rfiW9/6VtTV1UVnZ2evXhCDBg2KD33oQ3HqqacWrS6oJklTc+TtuLK9LdLOzsKFAquWR7Rszl+Dfg8AAAAAVKiKDB+eeeaZ/i7hgN74xjfGkCFD4uabb47t27fnrnd0dPS6d/r06fH+978/jjrqqFKWCJUto+dDpGnE9taIppEFeZvMLZdq6yJOOa0g7wEAAAAAA01Fhg+tra39XUKfnHfeeTFz5sy4//77Y8GCBfHSSy9FV1dXNDY2xpgxY+KUU06JM888M6ZMmdLfpULlydp2KSKidWvhwodFv88/cML0SIYMLch7AAAAAMBAU5Hhw7/9278V/JmH0+Nhf5qamuLiiy+Oiy++uCjPBzIMb8oeK1DT6XTThogXV+YdS2admfc6AAAAAFSCmgPfAlB5krq6iGGNecfSAjWdTp94JPv9ZwgfAAAAAKhcwgegemVtvVSolQ9ZWy5NOjaS0WMK8h4AAAAAMBAJH4Dq1VS88CFt3xHx7OK8Y7ZcAgAAAKDSCR+AqpU0jsg/UIiVD08/HtHZkf99ZwofAAAAAKhswgegemWsfChEz4d04cP5B0aMijj6uMN+PgAAAAAMZMIHoHplrXw4zPAh7eyM9MkFeceSGXMiqfGjFwAAAIDK5hMwoHoVq+H08mcjtrXlHbLlEgAAAADVQPgAVK2kSA2n00UZWy41DIo4ccZhPRsAAAAAyoHwAaheWdsu7doZ6a6dh/zYzPDh5NmRNAw65OcCAAAAQLkQPgDVK2vbpYhDXv2Qrlsd8fJLeceSmXMO6ZkAAAAAUG6ED0D1ytp2KeLQw4dFj+QfSJJIZpxxSM8EAAAAgHIjfACq15BhEXV1+cdaDzV8+H3+gSknRNI08pCeCQAAAADlRvgAVK0kSTL7PqRtLQf9vHRba8SyZ/O/18wzD/p5AAAAAFCuhA9Adcvq+9DactCPShc/GpF25R0TPgAAAABQTYQPQHXL6vtwCD0fMrdcGjM+4sijDvp5AAAAAFCuhA9AVUsyVz4cXPiQ7tkT8eTj+d9j5pl7t3gCAAAAgCohfACqW6F6Pjy3OGJXe94hWy4BAAAAUG2ED0B1K9C2S+mih/MPDB0WMfXkgywKAAAAAMqb8AGoblnbLh1E+JCmaaRP5A8fklNPj6Su7lAqAwAAAICyJXwAqlqSse1StG2NtKurbw95cWXE5o35x2y5BAAAAEAVEj4A1S1r26Wurogd2/r0iMwtl2prIzn1tEMsDAAAAADKl/ABqG5ZKx8i+rz1Umb4cPypkQwdfvA1AQAAAECZEz4A1S2r50NERGvLAV+ebtkU8cKyvGPJjDmHWBQAAAAAlDfhA1DVkvr6iCHD8o6lrQde+ZA+8Uj2s/V7AAAAAKBKCR8AslY/tLUc8KWZWy5NnBzJmPGHXhMAAAAAlDHhA0BW0+kD9HxId7ZHPLMo75hVDwAAAABUM+EDQNbKhwNtu/T0woiOPXmHhA8AAAAAVDPhA1D1ksYRea+nB9h2KX0iY8ulphERx0w7rJoAAAAAoJwJHwAOYdultKsz0icW5B1LZsyJpMaPVwAAAACql0/HAA5l26UVSzLDCVsuAQAAAFDthA9A1UuaRuQf2N/Kh0UZWy7VN0ScNOuwawIAAACAciZ8AMjo+RDt2yPdszvvUGb4cNLMSAYNKkxdAAAAAFCmhA8AWT0fIvKufkjXr4lY+2Le2225BAAAAADCB4Dsng8R+cOHRY9k3p7MmFOIigAAAACgrAkfAIYOj6jJ+HGYp+l05pZLxx4fyYhRBSwMAAAAAMqT8AGoeklNTebqh7Stpef59m0RS5/K/xyrHgAAAAAgIoQPAHtlbb30im2X0icfjejqyntrMku/BwAAAACIED4A7JUVPrxy26WsLZdGj42YeExBSwIAAACAciV8AIiIpHFE/oFu2y6lHXv2rnzI9/qZZ0aSJIUvDAAAAADKkPABICKiKavnQ7eVD0ueimjfkfe+ZKYtlwAAAABgH+EDQERE04j817ttu5Q+8Uj+e4YMjTj+lMLXBAAAAABlSvgAELGfng8tERGRpmmkC3+f95bklNMiqasvUmEAAAAAUH6EDwCxv54PWyNN04iXXojYtD7/PbZcAgAAAIAehA8AEZk9H6KzI6J9e6SLHs4/XlMTyfTTi1cXAAAAAJQh4QNARPa2SxERrVuzw4epJ0cyrLE4NQEAAABAmRI+AETsP3x46fmIlUvyDiW2XAIAAACAXoQPABGRDBocMWhw3rGuB3+R/TrhAwAAAAD0InwA2Cdr9cNTj+W/fuRRkYybULx6AAAAAKBMCR8A9skKH9I072WrHgAAAAAgP+EDwD5NIw7q9mTmnOLUAQAAAABlTvgA8N+SgwkfhjdFTDmhaLUAAAAAQDkTPgDsk7XtUh7JjDmR1NQWsRgAAAAAKF/CB4B9DiZ80O8BAAAAADIJHwD26Wv4UFcXcfKsopYCAAAAAOVM+ADw3/rc8+HEmZEMHlLUWgAAAACgnAkfAPbp48oHWy4BAAAAwP4JHwD2aepj+DBjTpELAQAAAIDyJnwA2GdYU0SS7P+eo4+LZNQRpakHAAAAAMqU8AHgvyW1tRHDm/Z/jy2XAAAAAOCAhA8A3R2g70MyS/gAAAAAAAcifADobn/hw8gjIo6aUrpaAAAAAKBMCR8AukmaRmSPzTwzkgP1hAAAAAAAhA8APexn5UMyc04JCwEAAACA8iV8AOguK3wYNCTihBmlrQUAAAAAypTwAaCb5MTp+a/POjOS+voSVwMAAAAA5Un4ANDdcSdFnPiKFQ6Dh0Tyjvf1Tz0AAAAAUIbq+rsAgIEkSZKo+evrI/35jyNeXBkxvCmSN74tklFH9HdpAAAAAFA2hA8Ar5DU1UXyPy7u7zIAAAAAoGzZdgkAAAAAACgo4QMAAAAAAFBQwgcAAAAAAKCghA8AAAAAAEBBCR8AAAAAAICCEj4AAAAAAAAFJXwAAAAAAAAKSvgAAAAAAAAUlPABAAAAAAAoKOEDAAAAAABQUMIHAAAAAACgoIQPAAAAAABAQQkfAAAAAACAgqrr7wJKYdu2bfHzn/88FixYEGvWrIndu3fHEUccEaeffnpcdNFFMXLkyP4uMdauXRv33ntvLF68ODZs2BBJksSECRPinHPOiQsvvDDq6+v7u0QAAAAAAOiTJE3TtL+LKKYnnngivvrVr0Zra2ve8aFDh8bll18ep59+eokr+4O77747br/99ujs7Mw7PmHChPjEJz4R48ePL3FlfbNu3bro6urq7zIYgOrq6mLs2LG58/Xr10dHR0c/VgQAkJ95CwBQLsxb6Kuampp+/Uy5orddevbZZ+MLX/hCZvAQEbFjx4740pe+FIsWLSphZX/w05/+NG655ZbM4CEiYs2aNXHdddfF5s2bS1gZAAAAAAAcmorddmndunXxhS98Ifbs2ZO7duaZZ8Zb3/rWOPLII2PdunXxwx/+MB5//PHo7OyMr3/96/HFL34xRowYUbIa58+fH7fcckvuvKamJi666KJ47WtfG01NTbFkyZL4wQ9+EGvWrIktW7bE1772tbj22msjSZKS1QgAAAAAAAerYlc+3HbbbbF9+/bc+dve9rb4u7/7u5g2bVoMHz48pk6dGh//+MfjtNNOi4iIrVu3xg9/+MOS1bd79+7413/919i361VtbW1cffXV8Wd/9mcxYcKEGD58eJx22mnxmc98JkaPHh0REU899VT87ne/K1mNAAAAAABwKCoyfFi+fHnMnz8/d37KKafEu9/97l731dTUxAc/+MGora2NiIgHHnggWlpaSlLjvffeG5s2bcqdv/3tb88FId01NjbGn//5n+fO77rrrpLUBwAAAAAAh6oiw4d77723x/l73vOezHtHjx4dc+bMiYiIzs7OeOihh4pa2z733Xdf7rixsTHe9ra3Zd77qle9KkaOHBkREatWrYrnn3++yNUBAAAAAMChq8jw4bHHHssdT548OY477rj93v/6178+d1yKxtOrVq2KDRs25M7PPffcaGhoyLy/trY2Xvva1+bOFy5cWMzyAAAAAADgsFRc+PDyyy9HW1tb7nz27NkHfM20adNyTZyXLFlStNr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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.concat((sale,forecast),axis=0)\n",
    "data.columns=['销量','未来7月销量']\n",
    "data.index = pd.DatetimeIndex(data.index)\n",
    "data.plot()\n",
    "plt.title(\"本田汽车销量预测\") # 图形标题\n",
    "plt.rcParams['savefig.dpi'] = 300 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.savefig('sale.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "nasty-myanmar",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid=model.resid\n",
    "plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "#自相关图\n",
    "plot_acf(resid,lags=23).show()\n",
    "\n",
    "#解读：有短期相关性，但趋向于零。\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(resid,lags=11).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "disturbed-consolidation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(resid, line='q', fit=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "numerous-cable",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D-W检验的结果为： 2.1754281495653394\n"
     ]
    }
   ],
   "source": [
    "print('D-W检验的结果为：',sm.stats.durbin_watson(resid.values))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "opposite-camel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  0.876244   0.349232\n"
     ]
    }
   ],
   "source": [
    "# 方法一\n",
    "print('残差序列的白噪声检验结果为：',acorr_ljungbox(resid,lags=1))#返回统计量、P值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "backed-amendment",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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